When it comes to AI inference accelerators, NVIDIA has captured the market as drones, intelligent high-resolution sensors, network video recorders, portable medical devices, and other industrial IoT systems use NVIDIA Jetson Xavier NX. This might change as Flex Logix’s InferX X1 AI inference accelerator has been shown to outperform Jetson Xavier NX as well as Tesla T4. During the Linley Fall Conference 2020, Flex Logix showcased InferX X1 AI Inference Accelerator, its performance, and how it outperformed other edge inference chips. It is the most powerful edge inference coprocessor with high throughput, low latency, high accuracy, large model megapixels images, and small die for embedded computing devices at the edge. The estimated worst-case TDP (933MHz, YOLOv3) is 13.5W. The coprocessor operates on the INT8 or BF16 precision over a batch size of 1 for minimum latency. The nnMAX Reconfigurable Tensor Processor accelerator engine exists in the edge inference coprocessor- InferX X1. The nnMAX Reconfigurable Tensor Processor is optimized for AI […]
Arm introduced their very first microNPU (Micro Neural Processing Unit) for microcontrollers at the beginning of the year with Arm Ethos-U55 designed for Cortex-M microcontrollers such as Cortex-M55, and delivering 64 to 512 GOPS of AI inference performance or up to a 480x increase in ML performance over Cortex-M CPU inference. The company has now unveiled an update with Arm Ethos-U65 microNPU that maintains the efficiency of Ethos-U55 but enables neural network acceleration in higher performance embedded devices powered by Arm Cortex-A and Arm Neoverse SoCs. Arm Ethos-U65 delivers up to 1 TOPS, and as seen in the diagram enables features that can not be done with Ethos-U55 including object classification and real-time classification. Compared to Ethos-N78 NPU, the new microNPU offers less AI performance, but a significantly higher efficiency although AFAIK no quantified by Arm. The company says the development workflow remains the same with the use of the TensorFlow Lite Micro (TFLmicro) runtime that runs on a Cortex-M […]
Raspberry Pi Trading has just launched 32 different models of Raspberry Pi CM4 and CM4Lite systems-on-module, as well as the “IO board” carrier board. But the company has also worked with third-parties, and Gumstix, an Altium company, has unveiled four different carrier boards for the Raspberry Pi Compute Module 4, as well as a convenient CM4 to CM3 adapter board that enables the use of Raspberry Pi CM4 on all/most carrier boards for the Compute Module 3/3+. Raspberry Pi CM4 Uprev & UprevAI CM3 adapter board Gumstix Raspberry Pi CM4 Uprev follows the Raspberry Pi Compute Module 3 form factor but includes two Hirose connectors for Computer Module 4. The signals are simply routed from the Hirose connectors to the 200-pin SODIMM edge connector used with CM3. Gumstix Raspberry Pi CM4 Uprev is the same except it adds a Google Coral accelerator module. Gumstix Raspberry Pi CM4 Development Board Specifications: 2x stacking board DF40-series connector for Raspberry Pi Compute Module […]
Google Coral SBC was the first development board with Google Edge TPU. The AI accelerator was combined with an NXP i.MX 8M quad-core Arm Cortex-A53 processor and 1GB RAM to provide an all-in-all AI edge computing platform. It launched for $175, and now still retails for $160 which may not be affordable to students and hobbyists. Google announced a new model called Coral Dev Board Mini last January, and the good news is that the board is now available for pre-order for just under $100 on Seeed Studio with shipping scheduled to start by the end of the month. Coral Dev Board Mini specifications haven’t changed much since the original announcement, but we know a few more details: SoC – MediaTek MT8167S quad-core Arm Cortex-A35 processor @ 1.3 GHz with Imagination PowerVR GE8300 GPU AI/ML accelerator – Google Edge TPU coprocessor with up to 4 TOPS as part of Coral Accelerator Module System Memory – 2GB LPDDR3 RAM Storage – […]
Earlier this year, we covered some video analytics solutions based on AAEON UP Xtreme Edge embedded computer combining an Intel Whiskey Lake processor with Intel Movidius Myriad X AI accelerator modules, as well as video management & analytics software solutions from Milestones & SAIMOS, or aotu.ai BrainFrame. iWave Systems has now introduced a similar solution with Corazon-AI gateway capable of handling up to 8 IP cameras in real-time, but instead of relying on AI accelerators, the company leverages Xilinx Zynq Ultrascale+ Arm Cortex-A53/R5 FPGA MPSoC for AI inference. Corazon-AI gateway specifications: SoC – Xilinz Zynq Ultrascale+ ZU2, ZU3, ZU4 or ZU5 MPSoC Processing System (PS) Quad/Dual Arm Cortex-A53 @ 1.5GHz, dual Cortex-R5 @ 600MHz Arm Mali-400MP2 GPU @ 677MHz H.264/H.265 Video Encoder/Decoder Programming Logic (PL) Up to 256K Logic cells PL GTH Transceivers x 4 @ 12.5 Gbps System Memory 64bit, 2GB DDR4 with ECC for PS (upgradable) 32bit, 1GB DDR4 for PL (upgradable) Storage – 8GB eMMC Flash (upgradable), […]
While MediaTek is better known for their mobile SoCs and they also offers processors made for AIoT (AI + IoT) applications such as MediaTek i700, i500 or i300 SoCs. We only covered a few development / evaluation boards so far, with Pumpkin i500 SBC or Innocom SB30 EVK, but we’ve now got another one with VIA VAB-950 single board computer equipped with MediaTek i500 AIoT octa-core processor, up to 4GB LPDDR4, 16GB flash storage, HDMI, dual Fast Ethernet, dual-band 802.11ac Wi-Fi, Bluetooth 5.0, and support for 4G LTE cellular connectivity. VAB-950 SBC specifications: SOM-9X50 system-on-module SoC – MediaTek i500 octa-core processor with 4x Cortex-A73 @ 2.0GHz, 4x Cortex-A53 @ 2.0GHz Arm Mali-G72 GPU up to 800 MHz with support for OpenGL ES 3.0, OpenCL ES 1.1, and Vulkan 1.0 VPU for 1080p30 H.264/H.265 decoding 2x Cadence Tensilica Vision P6 DSPs System Memory – 2GB or 4GB LPDDR4 SDRAM Storage – 16GB eMMC Flash memory Dimensions – 7 x 5.5 mm […]
Last year, Imagination Technologies unveiled IMG A-Series GPU family scaling from low-power IoT to mobile and high-performance server applications with up to 2.5 times the performance of the earlier PowerVR 9-series GPUs, as well as eight times faster AI processing and 60% less power under similar conditions. While I’m not aware of any SoCs announced with the new IMG A-Series GPU yet, the company has already announced the next-gen IMG B-Series GPU family with up to 4 times the multi-core performance thanks to decentralized multi-core technology, 30% lower power consumption, and 2.5 times the fill rate. The company offers four types of IM B-series GPU, each optimized for specific applications IMG BXE for high-resolution displays – From 1 up to 16 pixels per clock (PPC) BXE scales from 720p to 8K for UI rendering and entry-level gaming. IMG BXM designed for mid-range mobile gaming and complex UI solutions for DTV and other markets. IMG BXT four-core high-performance GPU generating 6.0 […]
Announced last January at CES 2020, Arduino Portenta H7 is the first board part industrial-grade “Arduino Pro” Portenta family. The Arduino MKR-sized MCU board has plenty of processing power thanks to STMicro STM32H7 dual-core Arm Cortex-M7/M4 microcontroller. It was launched with a baseboard providing access to all I/Os and ports like Ethernet, USB, CAN bus, mPCIe socket (USB), etc… But as AI moves to the very edge, it makes perfect sense for Arduino to launch Portenta Vision Shield with a low-power camera, two microphones, and a choice of wired (Ethernet) or wireless (LoRA) connectivity for machine learning applications. Portenta Vision Shield key features and specifications: Storage – MicroSD card socket Camera – Himax HM-01B0 camera module with 324 x 324 active pixel resolution with support for QVGA Image sensor – High sensitivity 3.6μ BrightSense pixel technology Microphone – 2x MP34DT05 omnidirectional microphones Connectivity Ethernet version- 10/100M Ethernet RJ45 jack LoRa version – Same Murata CMWX1ZZABZ LoRa module as found on […]
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