IFTTT (If This Than That), a web-based software that automates and connects over 600 online services/software raised a $24M Series C led by Salesforce. Other investors include IBM and the Chamberlain Group and Fenox Venture Capital.

The latest round brings IFTTT’s funding to $63M and it will use the funding proceeds to provide integration for enterprise and IoT services and hiring. In IFTTT’s platform, applets are code/script users need to deploy to integrate two or more services (such Google Drive’s integration with Twitter/Facebook).

Investment in IFTTT reveals that Salesforce is consolidating its presence in enterprise IoT space. It also acquired Mulesoft, an integration platform that rivals Microsoft’s BizTalk.

IBM’s investment in IFTTT is also noteworthy as the former is pushing its IBM Watson IoT platform. The following statement also shows its keen interest in IFTTT.

Other recent investments in IoT companies include $30M Series B of Armis and Myriota's $15M for its IoT satellite-based connectivity platform.

For latest IoT funding and product news, please visit our IoT news section.

Owlet, a connected-baby care company raised a $24 million Series B investment from Trilogy Equity Partners, with participation from existing investors, including Eclipse Ventures, Broadway Angels, and Enfield Ventures, and the addition Pelion Venture Partners.

Owlet’s core product is a baby sock that contains a smart sensor. The sensor monitors pulse oximetry, a technology used in hospitals to measure an infant's heart rate and blood oxygen levels. The vital signs are communicated to parents’ smartphone via Bluetooth connection. The product retails for $299 and parents can also choose from monthly payment plans.

Other IoT-health startups using sensors to monitor vital signs include Aifloo, a company selling wrist-bands for elders and Air by Propeller, a smart health company that provides an API to predict local asthma conditions.

A complete product package of Owlet monitor includes three fabric socks, smart sock sensor, a base station (which rings an alarm if a baby’s vital signs are abnormal), and charging cords.

Before the company raised the latest round, it closed a $15 million Series B round in late 2016. The recent investment brought Owlet’s total equity funding to $46M. Owlet plans to use the growth capital to launch more baby care products.

Vesper, the maker of piezoelectric sensors used in microphone production and winner of CES Innovation Award 2018 raised a $23M Series B round. American Family Ventures led the investment with participation from Accomplice, Amazon Alexa Fund, Baidu, Bose Ventures, Hyperplane, Sands Capital, Shure, Synaptics, ZZ Capital and some undisclosed investors.

Vesper’s innovative sensors can be used in consumer electronics like TV remote controls, smart speakers, smartphones, intelligent sensor nodes, and hearables. The company will use the funding proceeds to scale-up its functions like mass production of its microphones and support expanded research and development, hiring, and establishing international sales offices.

The main product of Vesper is VM1000, a low noise, high range,single-ended analog output piezoelectric MEMS microphone. It consists of a piezoelectric sensor and circuitry to buffer and amplify the output.

The hot-selling product of Vesper is VM1010 with ZeroPower Listening which is the first MEMS microphone that enables voice activation to battery-powered consumer devices.

The unique selling point of Vesper’s products is they are built to operate in rugged environments that have dust and moisture.

"Vesper's ZeroPower Listening capabilities coupled with its ability to withstand water, dust, oil, and particulate contaminants enables users that have never before been possible," said Katelyn Johnson, principal of American Family Ventures. "We are excited about Vesper's quest to transform our connected world, including IoT devices."

Other recent funding news include $24 raised by sensor-based baby sock maker Owlet, IFTTT banks $24M from Salesforce to scale its IoT Enterprise offering, and Intel sells its Wind River Software to TPG.

Smart baby monitor company Nanit raised a $14M Series B round led by Jerusalem Venture Partners (JVP). Other investors that participated include existing investors Upfront Ventures, RRE Ventures, Vulcan Capital and Vaal Investment Partners. The latest investment brings total equity funding of Nanit to $30M.

Nanit announced it will use the funding proceeds to expand its team of computer vision and machine learning engineers and grow its sales in Europe and Canada.

Nanit’s baby monitor helps new parents oversee nursery conditions as it has built-in temperature and humidity sensors. The camera lets parents remotely monitor baby’s crib whereas sound and motion are detected via smart sensors.

The monitor’s insights can be accessed via an accompanying mobile app. Nanit charges $10 per month for its premium package.

The key use cases of Nanit’s baby monitoring technology include sleep insights, behavioral analysis, expert guidance, and nightly video summaries. The company currently sells its smart monitors via its website.

Microsoft announced it has acquired Semantic Machines, a conversational AI startup providing chatbots and AI chat apps founded in 2014 having $20.9 million in funding from investors. The acquisition will help Microsoft catch up with Amazon Alexa, though the latter is more focused on enabling consumer applications of conversational AI.

Microsoft will use Semantic Machine’s acquisition to establish a conversational AI center of excellence in Berkeley to help it innovate in natural language interfaces.

Microsoft has been stepping up its products in conversational AI. It launched the digital assistant Cortana in 2015, as well as social chatbots like XiaoIce. The latest acquisition can help Microsoft beef up its ‘enterprise AI’ offerings.

As the use of NLP (natural language processing) increases in IoT products and services, more startups are getting traction from investors and established players. In June last year, Josh.ai, avoice-controlled home automation software has raised $8M.

Followed by it was SparkCognition that raised $32.5M Series B for its NLP-based threat intelligence platform.

It appears Microsoft’s acquisition of Semantic Machines was motivated by the latter’s strong AI team. The team includes technology entrepreneur Daniel Roth who sold his previous startups Voice Signal Technologies and Shaser BioScience for $300M and $100M respectively. Other team members include Stanford AI Professor Percy Liang, developer of Google Assistant Core AI technology and former Apple chief speech scientist Larry Gillick.

Arduino’s MKR family of products got two new wireless connectivity boards added to its range of products. These include MKR WiFi 1010 and MKR NB 1500, both aimed at streamlining IoT product/service development.

Arduino’s blog notes that “the Arduino MKR WiFi 1010 is the new version of the MKR1000 with ESP32 module on board made by U-BLOX.”

MKR WiFi 1010: For prototyping of WI-FI based IoT applications

The core difference of MKR WiFi 1010 compared to MKR WiFi 1000 is that the former can be put to use in production-grade IoT apps and it has ESP32-based module manufactured by u-blox. The former enables to add 2.4GHz WiFi and Bluetooth capability to the application. Additionally, it comes with a programmable dual-processor system (an ARM processor and a dual-core Espressif IC).

MKR NB 1500: For on-field monitoring systems and remote-controlled LTE-enabled modules

The Arduino MKR NB 1500 is based on new low-power NB-IoT (narrowband IoT) standard. This makes it appropriate for IoT apps running over cellular/LTE networks.

Key use cases of this board are remote monitoring systems and remote-controlled LTE-enabled modules. It supports AT&T, T-Mobile USA, Telstra, Verizon over the Cat M1/NB1 deployed bands 2, 3, 4, 5, 8, 12, 13, 20 and 28.

Arduino also pitches this board to be used in IoT apps which used to rely on alternative IoT networks such as LoRa and Sigfox. It promises to save power compared to GSM or 3G cellular-based connections.

Siemens Building Technologies division announced it will acquire Enlighted Inc., a smart IoT building technology provider. The transaction is expected to close in Q3’18.

Enlighted Inc.’s core element is an advanced lighting control application. It is based on a patented, software-defined smart sensor that collects and monitors real-time occupancy, light levels, temperatures and energy usage.

The sensor can gauge temperature, light level, motion, energy, and has Bluetooth connectivity.

The Enlighted system works by collecting temperature, light and motion data via its smart sensors. A gateway device carries the information to Energy Manager, a secure browser-based interface to create profiles and adjust settings of the entire Enlighted Advanced Lighting Control System. The Energy manager operates as an analytics device.

The whole system consists of multi-function sensors, distributed computing, a network, and software applications run by Enlighted Inc.

Enlighted Inc.’s main target market is commercial real estate. Key use cases of its intelligent Lighting Control System are energy efficiency, controlling heating, ventilation and air conditioning, and building utilization reports.

Use the Postscapes 'Connected Products Framework' to understand the smart home and buildings eco-system.

This new Corporate Social Responsibility (CSR) Practices in India Report 2020 is a must read

Report on Corporate Social Responsibility (CSR) in Indian BFSI sector.

EV Ultimo launches platform to assist brands, buyers, stakeholders in the Electric Vehicles ecosystem

[SAPS] Office of the Provincial Commissioner KwaZulu-Natal

[SAPS] - A former company Director (57) is expected to appear in the Thabamoopo Magistrates Court in Lebowakgomo on 11 November 2024 for allegedly defrauding a pensioner an amount of R378 000.00 in the name of business.

[SAPS] - Polokwane based Hawks Serious Commercial Crime Investigation in collaboration with National Traffic Anti-corruption Unit arrested 11 suspects between the ages of 27 and 57 for alleged fraud at various Provinces during operation "SISFIKILE".

[SAPS] - Five suspects are appearing in the Kariega Magistrate's Court today, after they were arrested and found in possession of cycads with an estimated value of R1 Million on Friday 08 November 2024.

[GroundUp] Former Lavender Hill gangster died on 29 October

[allAfrica] We are excited to share the momentous news that Cape Town Pride has officially won the bid to host WorldPride 2028. This significant event is a global celebration of LGBTQ+ pride and rights, marking a pivotal milestone not only for the LGBTQ+ community in the city but also for the entire African continent. This victory positions Cape Town as a leading symbol of inclusivity and diversity, showcasing its commitment to advancing a welcoming environment for all.

[DA] Note to editors: Please find attached soundbite by Ian Cameron MP.

[SAnews.gov.za] Twenty-three people in Gauteng have died as a result of food borne-related poisoning after consuming food from spaza shops.

[SAnews.gov.za] With the United Nations Framework Convention on Climate Change (COP29) taking place this week, South Africa expects the COP29 Presidency to enhance efforts to finalise the New Collective Quantified Goal on Finance (NCQG), which is a matter of great importance for developing economies.

[State Department] U.S. Ambassador-at-Large to Monitor and Combat Trafficking in Persons Cindy Dyer will travel to Madagascar November 13-16 and South Africa November 17-21.

[Namibian] Russian company Rosatom and South African AllWeld Nuclear and Industrial are joining forces to promote the sustainable development of nuclear energy in Africa.

[spotlight] Highly effective treatments for HIV have existed since the mid-1990s. But while these treatments keep people healthy, we do not yet have a safe and scalable way to completely rid the body of the virus. In this Spotlight special briefing, Elri Voigt takes stock of where we are in the decades-long search for an HIV cure.

[DA] It is a national embarrassment that the inability of the City of Johannesburg to supply water to its residents, business and public sector offices, has now led to the shutdown of operations at the Constitutional Court, on Constitution Hill in Braamfontein.

[GroundUp] The education department says there's only one SETA official assisting all nine provinces

The market is trending towards integrating and stacking multiple chiplets into a single package to meet the growing demands of speed, connectivity, and intelligence.  However, designing and signing off chiplets and packages individually is time-...(read more)

PCI-SIG DevCon 2024 was a great success for Cadence. We posted the blog, Cadence Demonstrates Complete PCIe 7.0 Solution at PCI-SIG DevCon ‘24 a day before the event to advertise our IP solutions for PCIe 7.0, which resulted in a lot of extra traffic at our booth. All of the attendees were excited to see Cadence demonstrate the robustness of 128GT/s PCIe 7.0 IP's TX and RX capabilities over a real-world, low-latency, non-retimed, linear optics connector. We achieved and maintained a consistent, impressive pre-FEC BER of ~3E-8 (PCIe spec requires 1E-6) for the entire duration of the event, spanning over two full days with no breaks. This provides an ample margin for RS FEC. As seen in the picture below, the receiver Eye PAM4 histograms have good linearity and margin. This is the world’s first stable demonstration of 128 GT/s TX and RX over off-the-shelf optical connectors—by far the main attraction of DevCon this year.

Cadence 128 GT/s TX and RX capability over optics

Block diagram of Cadence PHY for PCIe 7.0 128 GT/s demo setup with linear pluggable optics

As a leader in PCIe, our PCIe controller architect Anish Mathew shared his valuable insights on an important topic: “Impact of UIO ECN on PCIe Controller Design and Performance,” highlighting the strides made by the Cadence design team in achieving this implementation.

Anish Mathew presenting “Impact of UIO ECN on PCIe Controller Design and Performance”

In summary, Cadence had a dominating presence on the demo floor with a record number of PCIe demos:

• PCIe 7.0 over optics
• PCIe 7.0 electrical
• PCIe 6.0 RP/EP interop back-to back
• PCIe 6.0 protocol in FLIT mode with Lecroy Exerciser (at Cadence booth)
• PCIe 6.0 protocol in FLIT mode (at the Lecroy booth)
• PCIe 6.0 JTOL with Anritsu and Tektronix equipment (at Tektronix booth)
• PCIe 6.0 protocol with Viavi Protocol Analyzer (at Viavi booth)
• PCIe 6.0 System Level Interop Demo with Gen5 platform (at SerialTek booth)

The Cadence team and its partners did a great job in coordinating and setting up the demos that worked flawlessly. This was the culmination of many weeks of hard work and dedication. Four different vendors featured our IP for PCIe 6.0. They attracted a lot of attention and drove traffic back to us.

Highlights of Cadence demos for PCIe 7.0 and 6.0

Cadence team at the PCI-SIG Developers Conference 2024

Thanks to everyone who attended the 32nd PCI-SIG DevCon. We really appreciate your interest in Cadence IP, and a big thanks to our partners and customers for all the positive feedback and for creating so much buzz for the Cadence brand.

The automotive industry is currently on the cusp of a radical evolution, steering towards a future where cars are not just vehicles but sophisticated, software-defined vehicles (SDV). This shift is marked by an increased reliance on automation and a significant increase in the use of sensors to improve safety and reliability. However, the increasing number of sensors has led to higher compute demands and poses challenges in managing a wide variety of data. The traditional method of using separate processors to manage each sensor's data is becoming obsolete. The current trends necessitate a unified processing system that can deal with multimodal sensor data, utilizing traditional Digital Signal Processing (DSP) and AI-driven algorithms. This approach allows for more efficient and reliable sensor fusion, significantly enhancing vehicle perception. Developers often face difficulties adhering to stringent power, performance, area, and cost (PPAC) and timing constraints while designing automotive SoCs.

Cadence, with its groundbreaking products and AI-powered processors, is enabling designers and automotive manufacturers to meet the future sensor fusion demands within the automotive sector. At the recent CadenceLive Silicon Valley 2024, Amol Borkar, product marketing director at Cadence, showcased the company's dedication and forward-thinking solutions in a captivating presentation titled "Addressing Tomorrow’s Sensor Fusion Needs in Automotive Computing with Cadence." This blog aims to encapsulate the pivotal takeaways from the presentation. If you missed the chance to watch this presentation live, please click here to watch it.

Significant Trends in the Automotive Market – Industry Landscape

We are witnessing a revolution in automotive technology. Innovations like occupant and driver monitoring systems (OMS, DMS), 4D radar imaging, LiDAR technology, and 360-degree view are pushing the boundaries of what's possible, leading us into an era of remarkable autonomy levels—ranging from no feet or hands required to eventually no eyes needed on the road.

Sensor Fusion and Increasing Processing Demands—Sensor fusion effectively integrates data from different sensors to help vehicles understand their surroundings better. Its main benefit is in overcoming the limitations of individual sensors. For example, cameras provide detailed visual information but struggle in low-light or lousy weather. On the other hand, radar is excellent at detecting objects in these conditions but lacks the detail that cameras provide. By combining the data from multiple sensors, automotive computing can take advantage of their strengths while compensating for their weaknesses, resulting in a more reliable and robust system overall.

One thing to note is that the increased number of sensors produces various data types, leading to more pre-processing.

On-Device Processing—As the industry moves towards autonomy, there is an increasing need for on-device data processing instead of cloud computing to enable vehicles to make informed decisions. Embracing on-device processing is a significant advancement for facilitating real-time decisions and avoiding round-trip latency.

AI Adoption—AI has become integral to automotive applications, driving safety, efficiency, and user experience advancements. AI models offer superior performance and adaptability, making future-proofing a crucial consideration for automotive manufacturers. AI significantly enhances sensor fusion algorithms, offering scalability and adaptability beyond traditional rule-based approaches. Neural networks enable various fusion techniques, such as early fusion, late fusion, and mid-fusion, to optimize the integration and processing of sensor data.

Future Sensor Fusion Needs

Automotive architectures are continually evolving. With current trends and AI integration into radar and sensor fusion applications, SoCs should be modular, flexible, and programmable to meet market demands.

Heterogeneous Architecture- Today's vehicles are loaded with various sensors, each with a unique processing requirement. Running the application on the most suitable processor is essential to achieve the best PPA. To meet such requirements, modern automotive solutions require a heterogeneous compute approach, integrating domain-specific digital signal processors (DSPs), neural processing units (NPUs), central processing unit (CPU) clusters, graphics processing unit (GPU) clusters, and hardware accelerator blocks. A balanced heterogeneous architecture gives the best PPA solution.

Flexibility and Programmability- The industry has come a long way from using computer vision algorithms such as HOG (Histogram Oriented Gradient) to detect people and objects, HAR classifier to detect faces, etc., to CNN and LSTM-based AI to Transformer models and graphical neural networks (GNN). AI has evolved tremendously over the last ten years and continues to evolve. To keep up with the evolving rate of AI, SoC design must be flexible and programmable for updates if needed in the future.

Addressing the Sensor Fusion Needs with Cadence

Cadence offers a complete suite of hardware and software products to address the increasing compute requirements in automotive. The comprehensive portfolio of Tensilica products built on the robust 32-bit RISC architecture caters to various automotive CPU and AI needs. What makes them particularly appealing is their scalability, flexibility, and configurability, offering many options to meet diverse needs.

The Xtensa family of products offers high-quality, power-efficient CPUs. Tensilica family also includes AI processors like Neo NPUs for the best power, performance, and area (PPA) for AI inference on devices or more extensive applications. Cadence also offers domain-specific products for DSPs such as HIFI DSPs, specialized DSPs and accelerators for radar and vision-based processing, and a general-purpose family of products for floating point applications.

The ConnX family offers a wide range of DSPs, from compact and low-power to high-performance, optimized for radar, lidar, and communications applications in ADAS, autonomous driving, V2X, 5G/LTE/4G, wireless communications, drones, and robotics. Tensilica's ISO26262 certification ensures compliance with automotive safety standards, making it a trusted partner for advanced automotive solutions. The Cadence NeuroWeave Software Development Kit (SDK) provides customers with a uniform, scalable, and configurable ML interface and tooling that significantly improves time to market and better prepares them for a continuously evolving AI market. Cadence Tensilica offers an entire ecosystem of software frameworks and compilers for all programming styles.

Tensilica's comprehensive software stack supports programming for DSPs, NPUs, and accelerators using C++, OpenCL, Halide, and various neural network approaches. Middleware libraries facilitate applications such as SLAM, radar processing, and Eigen libraries, providing robust support for automotive software development.

Conclusion

Cadence’s Tensilica products offer a development toolchain and various IPs tailored for the automotive industry, covering audio, vision, radar, unified DSPs, and NPUs. With ISO certification and a robust partner ecosystem, Tensilica solutions are designed to meet the future needs of automotive computing, ensuring safety, efficiency, and innovation.

Learn More

• Cadence Automotive Solutions
• Cadence Automotive IP
• Sensor Fusion and ADAS in TSMC Automotive Processes
• Revolution on the Road: How Cadence is Driving the Future of Automotive Design!
• Taming Design Complexity in Chiplet-Based Automotive Electronics
• UCIe and Automotive Electronics: Pioneering the Chiplet Revolution

The generative AI market is experiencing rapid growth, driven by the increasing parameter size of Large Language Models (LLMs). This growth is pushing the boundaries of performance requirements for training hardware within data centers. For an in-depth look at this, consider the insights provided in "HBM3E: All About Bandwidth". Once trained, these models are deployed across a diverse range of applications. They are transforming sectors such as finance, meteorology, image and voice recognition, healthcare, augmented reality, high-speed trading, and industrial, to name just a few.

The critical process that utilizes these trained models is called AI inference. Inference is the capability of processing real-time data through a trained model to swiftly and effectively generate predictions that yield actionable outcomes. While the AI market has primarily focused on the requirements of training infrastructure, there is an anticipated shift towards prioritizing inference as these models are deployed.

The computational power and memory bandwidth required for inference are significantly lower than those needed for training. Inference engines typically need between 300-700GB/s of memory bandwidth, compared to 1-3TB/s for training. Additionally, the cost of inference needs to be lower, as these systems will be widely deployed not only in data centers but also at the network's edge (e.g., 5G) and in end-user equipment like security cameras, cell phones, and automobiles.

When designing an AI inference engine, there are several memory options to consider, including DDR, LPDDR, GDDR, and HBM. The choice depends on the specific application, bandwidth, and cost requirements. DDR and LPDDR offer good memory density, HBM provides the highest bandwidth but requires 2.5D packaging, and GDDR offers high bandwidth using standard packaging and PCB technology.

The GDDR7 standard, announced by JEDEC in March of this year, features a data rate of up to 192GB/s per device, a chip density of 32Gb, and the latest data integrity features. The high data rate is achieved by using PAM3 (Pulse Amplitude Modulation) with 3 levels (+1, 0, -1) to transmit 3 bits over 2 cycles, whereas the current GDDR6 generation uses NRZ (non-return-to-zero) to transmit 2 bits over 2 cycles.

GDDR7 offers many advantages for AI Inference having the best balance of bandwidth and cost. For example, an AI Inference system requiring 500GB/s memory bandwidth will need only 4 GDDR7 DRAM running at 32Gbp/s (32 data bits x 32Gbp/s per pin = 1024Gb/s per DRAM). The same system would use 13 LPDDR5X PHYs running at 9.6Gbp/s, which is currently the highest data rate available (32 data bits x 9.6Gb/s = 307Gb/s per DRAM).

Cadence stands at the forefront of AI inference hardware support, being the first IP company to roll out GDDR7 PHYs capable of impressive speeds up to 36Gb/s across various process nodes. This milestone builds on Cadence's established leadership in GDDR6 PHY IP, which has been available since 2019. The company caters to a diverse client base spanning AI inference, graphics, automotive, and networking equipment.

While GDDR7 continues to utilize standard PCB board technology, the increased signal speeds seen in GDDR6 (20Gbp/s) and now GDDR7 (36Gb/s) calls for careful attention with the physical design to ensure optimized system performance. In addition to providing the PHY, Cadence also offers comprehensive PCB and package reference design, which are essential in helping customers achieve optimal signal and power integrity (SI/PI) for their systems.

Cadence is dedicated to ensuring customer success beyond just providing hardware. They provide expert support in SI/PI, collaborating closely with customers throughout the design process. This approach ensures that customers can benefit from Cadence's expertise in navigating the complexities of high-speed design and achieving optimal performance in their AI inference systems.

As the AI market continues to advance, Cadence remains at the forefront by offering a comprehensive memory IP portfolio tailored for every segment of this dynamic market. From DDR5 and HBM3E, which cater to the intensive demands of training in servers and high-performance computing (HPC), to LPDDR5X designed for low-end inference at the network edge and in consumer devices, Cadence's offerings cover a wide range of applications.

Looking to the future, Cadence is dedicated to innovating at the forefront of memory system performance, ensuring that the evolving needs of AI training and inference are met with the highest standards of excellence. Whether it's pushing the boundaries with GDDR7 or exploring new technologies, Cadence is dedicated to driving the AI revolution forward, one breakthrough at a time.

Learn more about Cadence GDDR7 PHY

Learn more about Cadence Simulation VIP for GDDR7.

Today's high-performance computing systems often require the designer to instantiate multiple CPU or DSP cores in their subsystem. However, the performance gained by using multiple CPUs comes with additional programming complexity, especially when accessing shared memory data structures and hardware peripherals. CPU cores need to access shared data in an atomic fashion in a multi-core environment. Locking is the most basic requirement for data sharing. A core takes the lock, accesses the shared data structure, and releases the lock. While one core has the lock, other cores are disallowed from accessing the same data structure. Typically, locking is implemented using an atomic read-modify-write bus transaction on a variable allocated in an uncached memory.

This blog shares the AXI4 locking mechanism when implementing an Xtensa LX-based multi-core system on a Xilinx FPGA platform. It uses a dual-core design mapped to a KC705 platform as an example.

Exclusive Access to Accomplish Locking

The Xtensa AXI4 manager provides atomic access using the AXI4 atomic access mechanism. While Xtensa's AXI manager interface generates an exclusive transaction, the subordinate's interface is also expected to support exclusive access, i.e., AXI monitoring. Xilinx BRAM controller's AXI subordinate interface does not support exclusive access, i.e., AXI monitoring: AXI Feature Adoption in Xilinx FPGAs.

Leveraging Xtensa AXI4 Subordinate Exclusive Access

The Xtensa LX AXI subordinate interface supports exclusive access. One approach is to utilize this support and allocate locks in one of the core's local data memories. Ensure that the number of external exclusive managers is configured, typically to the number of cores (Figure 1).

Figure 1

Note that the Xtensa NX AXI subordinate interface does not support exclusive access. For an Xtensa NX design, shared memory with AXI monitoring is required.

In Figure 2, the AXI_crossbar#2 (block in green) routes core#0's manager AXI access (blue connection) to both core's local memories. Core#1's manager AXI (yellow connection) can also access both core's local memories. Locks can be allocated in either core's local data memory.

In-Bound Access on Subordinate Interface

On inbound access, the Xtensa AXI subordinate interface expects a local memory address, i.e., an external entity needs to present the same address as the core would use to access local memory in its 4GB address space. AXI address remap IP (block in pink) translates the AXI system address to each core's local address. For example, assuming locks are allocated in core#0's local memory, core#1 generates an AXI exclusive to access a lock allocated in core#0's local memory (yellow connection). AXI_crossbar#2 forwards transaction to M03_AXI port (green connection). AXI_address_remap#1 translates the AXI system address to the local memory address before presenting it to core#0's AXI subordinate interface (pink connection).

It is possible to configure cores with disjoint local data memory addresses and avoid the need for an address remap IP block. But then it will be a heterogeneous multi-core design with a multi-image build. An address remap IP is required to keep things simple, i.e., a homogeneous multi-core with a single image build. A single image uses a single memory map. Therefore, both cores must have the same view of a lock, i.e., the lock's AXI bus address must be the same for both.

Figure 2

AXI ID Width

Note Xtensa AXI manager interface ID width=4 bits. Xtensa's AXI subordinate interface ID width=12 bits. So, you must configure AXI crossbar#2 and AXI address remap AXI ID width higher than 4. AXI IDs on a manager port are not globally defined; thus, an AXI crossbar with multiple manager ports will internally prefix the manager port index to the ID and provide this concatenated ID to the subordinate device. On return of the transaction to its manager port of origin, this ID prefix will be used to locate the manager port, and the prefix will be truncated. Therefore, the subordinate port ID is wider in bits than the manager port ID. Figure 3 shows the Xilinx crossbar IP AXI ID width configuration.

Figure 3

Software Tools Support

Cadence tools provide a way to place locks at a specific location. For more details, please refer to Cadence's Linker Support Packages (LSP) Reference Manual for Xtensa SDK. .xtos.lock(green) resides in core#0's local memory and holds user-defined and C library locks. The lock segment memory attribute is defined as shared inner (cyan) so that L32EX and S32EX instructions generate an exclusive transaction on an AXI bus. See Figure 4. The stack and per-core Xtos and C library contexts are allocated in local data memory (yellow).

…………..LSP memory map………….
BEGIN dram0
0x40000000: dataRam : dram0 : 0x8000 : writable ;
dram0_0 : C : 0x40000400 - 0x40007fff : STACK : .dram0.rodata .clib.percpu.data .rtos.percpu.data .dram0.data .clib.percpu.bss .rtos.percpu.bss .dram0.bss;
END dram0
…………………
BEGIN sysViewDataRam0
0xA0100000: system : sysViewDataRam0 : 0x8000 : writable, uncached, shared_inner;
lockRam_0 : C : 0xA0100000 - 0xA01003ff : .xtos.lock;
END sysViewDataRam0
…………..

Figure 4

Please visit the Cadence support site for more information on emulating Xtensa cores on FPGAs.

LEF allows the same port with multiple shape definitions. Does anybody know if innovus can create multiple duplicate shapes associated with the same port? Assume they are connected outside the block with perfect timing synchronization. Thank you!

Hi,

Is there any way to instruct the tool to reduce the low metals effort and route more on top layers? 

Hi everyone,

I'm new to using Innovus and I'm encountering an issue while trying to perform the "init_design" command. My goal is to perform the place and route. Here are the commands I'm using:

``
set init_verilog ./test.v
set init_top_cell TEST
set init_pwr_net {VDD VDD_2 VDD_3}
set init_gnd_net {VSS VSSA}
set init_lef_file { /home/laumecha/uw_openroad_free45/pdk/Drexel-ECEC575/Encounter/NangateOpenCellLibrary/Back_End/lef/NangateOpenCellLibrary.lef}
set init_mmmc_file {./viewDefinition.tcl}
init_design
```

However, I receive the following error:

```
#% Begin Load netlist data ... (date=06/04 12:07:50, mem=1478.7M)
*** Begin netlist parsing (mem=1439.0M) ***
Created 0 new cells from 0 timing libraries.
Reading netlist ...
Backslashed names will retain backslash and a trailing blank character.
**ERROR: (IMPVL-209):   In Verilog file './test.v', check line 16 near the text # for the issue: 'syntax error'.  Update the text accordingly.
Type 'man IMPVL-209' for more detail.
Verilog file './test.v' has errors!  See above.

*** Memory Usage v#1 (Current mem = 1439.027M, initial mem = 634.098M) ***
#% End Load netlist data ... (date=06/04 12:07:50, total cpu=0:00:00.0, real=0:00:00.0, peak res=1478.7M, current mem=1478.7M)
**ERROR: (IMPVL-902):   Failed to read netlist ./test.v. See previous error messages for details.  Resolve the issues and reload the design.
```

However, the file works perfectly in Genus.

It seems there is a syntax error in my Verilog file at line 16, but I'm not sure how to resolve it. Any guidance or suggestions would be greatly appreciated.

Thanks in advance!

We are currently setting up the Tempus flow and have ran into some mismatched data regarding ECO and timing reports. I generated a timing report before running ECO and saw six total setup violations. When running opt_signoff -setup, the initial setup summary that was printed in the shell only showed one violation. I can see that violation from the initial setup summary in my pre-ECO timing report and it is not the worst path. Upon further investigation, I forced the tool to try to fix setup on one of the other five violations from the timing report using the opt_signoff_select_setup_endpoints attribute and the tool said that the endpoint had positive slack and would be ignored.

Has anyone experienced something like this before?

Hi, I'm using genus (Version 21.14-s082_1) to synthesis a VHDL-design with multiple power-domains. After reading the power intent file and calling 'apply_power_intent',  I get the following warning:

Warning : Potential problem while applying power intent of 1801 file. [1801-99]
: Cannot find any instance for scope '/:CHIP_TOP'. Rest of commands in this scope will be skipped (set_scope:../../upf/CHIP_TOP.upf:2).
: Check the power intent. If the scenario is expected, this message can be ignored.

The fist two lines of CHIP_TOP.upf:

upf_version 3.1
set_scope :CHIP_TOP

I simulated the same  UPF and VHDL files with Xeclium and was able to verify all the IEEE1801/UPF aspects I need without any problems. I don't know, why genus is having a problem with the 'scope'.
In genus, after getting the warning, running 'set_db power_domain:CHIP_TOP/BLOCK_A/PD_CORE_D .library_domain PD0V5' returns the following error:

Error : <Start> word is not recognized. [TUI-182] [set_db]
: 'power_domain:CHIP_TOP/BLOCK/PD_CORE_D' is not a recognized object/attribute. Type 'help root:' to get a list of all supported objects and attributes.
: Check if the given <Start> word is a valid object_type, object or attribute.

Running 'commit_power_intent' gives me:

Started inserting low power cells...
====================================
Info : Command 'commit_power_intent' cannot proceed as there are no power domains present. [CPI-507]
: Design with no power domains is 'design:CHIP_TOP'.
Completed inserting low power cells (runtime 0.00).
====================================================

I'm suspecting that the problem lies in 'set_scope' and VHDL. I never had such problems with Verilog. I tried every way to reference the hierarchy in the code and now I'm at my wit's end and I need your help o/
How to set the scope with 'set_scope' in UPD 3.1 to the toplevel in VHDL, so that genus accepts it? Or is the problem caused by something else?

Best,

Iqbal

Hi all, 

I have made a custom cell in Innovus that I will be instantiating into a bigger block, which I will also be using Innovus to do the Place & Route. 

I understand that I can generate a *.lef file and a *.lib file using Innovus. However, I need to also create a *.db file (these format of files are often used in DC Compiler synthesis tool). 

Is there a way to create the *.db file from Innovus? Or, is there a tool that I can use to create this *.db file? 

Thank you for your time. 

I'm trying to write a generic piece of code that will return the layermap file location, with file name, for a variety of projects (which could potential have different layermap file naming conventions. The below code is what I've used to date, but this assumes the file name is xxxx.layermap. I can obviously do some string matching to find it, assuming the various files all contain some common characters. I thought I'd ask if there is a simpler way to find it, I know that this information is automatically loaded into the Xstream out gui, so maybe I can use the same approach to find it.

techLibName=techGetTechFile(cv)~>libName

techLibLayerMap=strcat(ddGetObj(techLibName)~>readPath "/" techLibName ".layermap")

Hi everyone. 

I'm very a new cadence user. I'm not good at using it and quite lost in finding a way to get the results. With the topic, I would like to ask you for some suggestions to improve my cadence skills.

I have some digital decision logic. Some are combinational logic, some are sequential logic that I would like to import or generate random input combination to the inputs of my decision logic to get the maximum, minimum, and average delay power dissipation and area when feeding the different input combination.

My logic has 8-bit, 16-bit, and 32-bit input. The imported data tends to be decimal numbers.

I would like to ask you:

- which tool(s) are the most appropriate to import and feed the different combination to my decision logic?

- which tool is the most appropriate to synthesis with different number of input? - I have used Genus Synthesis Solution so far. However with my skill right now I can only let Genus synthesize my Verilog code one setup at a time. I'm not sure if I there is anyway I can feed a lot of input at a time and get those results (min, max, average of delay, power dissipation and area)

- which language or scripts I should pick up to use and achieve these results?

-where can I find information to solve my problem? which information shall I look for?

Thank you so much for your time!!

Best Regards

I have a 2-dimensional input in my design - input [2:0] data_in [15:0]. After synthesis with genus, I got a netlist where the inputs are like data[15], data[14],...,data[0]. And furthermore it has definitions like input [2:0] data[15], .... So how can I define the pin locations of each of the bits for this input? Can I define data[15]'s inner bits like data[15][0]? Is it possible to define this with def files?

Hi! I've made some 1-point waveform "markers" that I want to overlay in my plots to aid visualization (with the added advantage, w.r.t. normal Viva markers, that they update location automatically upon refreshing simulation data).

For example, the plot below shows an spectrum along with two of these markers, which I create with the function "singlePointWave", and the Assembler output definitions also as shown below.

The problem is: as currently created and defined, Assembler is unable to plot these elements. I can send their expressions to the calculator and plotting works from there, BUT ONLY after first enabling the "Allow Any Units" in the target Viva subwindow.

Thus, I suspect Assembler is failing to plot my markers because they "lack" other information like axes units and so on. How could I add whatever is missing, so that these markers can plot automatically from Assembler?

Thanks in advance for any help!

Jorge.

P.S. I also don't know why, but nothing works without those "ymax()" in the output definitions--I suspect they are somehow converting the arguments to the right data type expected by singlePointWave(). Ideas how to fix that are also welcome! ^^

procedure( singlePointWave(xVal yVal)
    let( (xVect yVect wave)
        xVect = drCreateVec('double list(xVal));
        yVect = drCreateVec('double list(yVal));
        wave = drCreateWaveform(xVect yVect);
    );
);

Hi,

I am looking to perform a net trace in a CDL file.

There is a net at a lower level and would like to know the net it is connected to at the top level.

Please let me know if there is a way to analyze the CDL file to perform this net trace.

Thanks,

Mallikarjun.

I want to create sub tree of sub trees how can i do that?

I want to create another tree for the Sub parameter also.

Is there a skill command for "Assign Layout Instance terminals", this form appears when i click on define device correspondence and Bind the devices.

Also,

Problem Statement : i have a schematic with a couple of transistor symbols and and i alos have a corresponding layout view with respective layout transistors but they all are inside a pCell(created by me) i.e layout transistor called inside a custom Pcell. Now i have multiple symbols in schematic view and a single instance(pCell) in layout view. 
Is there a way how i can bind these schematic symbols with layout symbols inside the pCell(custom)? Even if i have to use cph commands i'm fine with it. need help here.

The idea here is to establish XL connectivity between the schematic symbols and corresponding layout transistors(inside the pCell).

Thanks,

Shankar

Hello Community,

Is there any simple way how i can get the coordinates of all the shapes in a layout view?

Currently i'm flattening the layout, getting all the lpps from CV and using setof to get all the shapes of a layer and looping through them to get the coordinates.

Is there a way to do it without having to flatten the layout view and shapes merged or any other elegant way to do it if we flatten it?

Also, dbWriteSkill doesn't give output how i desired

Thanks,

Shankar