ESWIN EIC7700X quad-core RISC-V SoC embeds 19.95 TOPS NPU for Edge AI vision applications

ESWIN EIC7700X block diagram

Yesterday we noted Sipeed was working on the LM5A system-on-module powered by ESWIN EIC7700X quad-core RISC-V processor with a ~20 TOPS AI accelerator in order to integrate it into its Lichee Book laptop and other carrier boards. So today, I’ve decided to look into the EIC7700X SoC designed by “BEIJING ESWIN COMPUTING TECHNOLOGY CO., LTD”, or ESWIN for shorts. The EIC770X features four 64-bit RISC-V (RV64GC) cores clocked up to 1.8 GHz, unnamed 3D and 2D GPUs, a 19.95 TOPS NPU, H.265/H.264 video encoder/decoder capable of handling up to 32x 1080p30 videos, various video output (HDMI + DSI) and input interfaces, dual GbE, 4-lane PCIe Gen 3, and more.   ESWIN EIC7700X specifications: CPU 4x SiFive Performance P550 RV64GC RISC-V cores @ 1.4GHz (up to 1.8GHz) with Cortex-A75-class performance 32KB(I) + 32KB(D) L1 Cache 256KB L2 Cache 4MB shared L3 Cache Cache supports ECC (support SECDED) DNN Accelerator – 19.95 […]

Cavli C17QS Cat 1.bis cellular IoT and GNSS module offers more memory, global support, a new FreeRTOS SDK

Cavli C17QS Cat 1.bis cellular IoT module with FreeRTOS SDK

Cavli Wireless C17QS is a Cat 1.bis cellular IoT and GNSS module that builds up on the Qualcomm QCX216-powered C16QS Cat 1.bis Cellular IoT module introduced last year, with more memory (2MB RAM) and storage (8MB flash), a wider range of LTE bands, multi-band (L1 and L5) GNSS, and a new FreeRTOS SDK for more flexibility compared to the C16QS. The Cavli C17QS Cat 1bis module is designed around a Qualcomm QCX217 Arm Cortex-M3 microcontroller clocked at up to 306MHz clock speed and running FreeRTOS real-time operating system. The module features a range of interfaces including UART, USB 2.0, USIM, SWD, ADCs, I2S, I2C, SPI, and GPIO pins. The new module is pin-to-pin compatible with the C16QS module for easier design upgrades. Cavli C17QS specifications: Wireless IC – Qualcomm QCX217 Arm Cortex-M3 @ 306 MHz, cellular modem-RF Memory – 2MB RAM Storage – 8 MB flash Cellular connectivity LTE CAT […]

picoLLM is a cross-platform, on-device LLM inference engine

picoLLM Raspberry Pi 5

Large Language Models (LLMs) can run locally on mini PCs or single board computers like the Raspberry Pi 5 but with limited performance due to high memory usage and bandwidth requirements. That’s why Picovoice has developed the picoLLM Inference Engine cross-platform SDK optimized for running compressed large language models on systems running Linux (x86_64), macOS (arm64, x86_64), and Windows (x86_64), Raspberry Pi OS on Pi 5 and 4, Android and iOS mobile operating systems, as well as web browsers such as Chrome, Safari, Edge, and Firefox. Alireza Kenarsari, Picovoice CEO, told CNX Software that “picoLLM is a joint effort of Picovoice deep learning researchers who developed the X-bit quantization algorithm and engineers who built the cross-platform LLM inference engine to bring any LLM to any device and control back to enterprises”. The company says picoLLM delivers better accuracy than GPTQ when using Llama-3.8B MMLU (Massive Multitask Language Understanding) as a […]

EdgeCortix SAKURA-II Edge AI accelerator deliver up to 60 TOPS in an 8W power envelope

SAKURA-II M.2 and PCIe Edge AI accelerators

EdgeCortix has just announced its SAKURA-II Edge AI accelerator with its second-generation Dynamic Neural Accelerator (DNA) architecture delivering up to 60 TOPS (INT8) in an 8Watts power envelope and suitable to run complex generative AI tasks such as Large Language Models (LLMs), Large Vision Models (LVMs), and multi-modal transformer-based applications at the edge. Besides the AI accelerator itself, the company designed a range of M.2 modules and PCIe cards with one or two SAKURA-II chips delivering up to 120 TOPS with INT8, 60 TFLOPS with BF16 to enable generative AI in legacy hardware with a spare M.2 2280 socket or PCIe x8/x16 slot. SAKURA-II Edge AI accelerator SAKURA-II key specifications: Neural Processing Engine – DNA-II second-generation Dynamic Neural Accelerator (DNA) architecture Performance 60 TOPS (INT8) 30 TFLOPS (BF16) DRAM – Dual 64-bit LPDDR4x (8GB,16GB, or 32GB on board) DRAM Bandwidth – 68 GB/sec On-chip SRAM – 20MB Compute Efficiency – […]

Avnet AI Vision Development Kit features Qualcomm QCS6490 SoC, dual camera, GbE, and USB-C PD

QCS6490 Vision AI Development Kit

Just last month at Embedded World 2024, Qualcomm announced its RB3 Gen 2 Platform based on the QCS6490 processor with Cortex-A78 and A55 processing cores and 12 TOPS of AI power. Building on this, Avnet has recently launched the Avnet AI Vision Development Kit, also based on the QCS6490 SoC. The kit includes a dual camera setup, Gigabit Ethernet connectivity, USB-C Power Delivery, and a host of other features for applications like inventory and asset monitoring, drone/UAV/other mobile vision-AI edge compute applications, and multi-camera security systems with recognition. Previously we have written about similar AI dev kits including Allwinner V853 100ASK-V853-Pro, Sipeed Maix-III devkit, RZBoard V2L, and many other AI vision development boards feel free to check those out if you are interested in the topic. Avnet AI Vision Development Kit specifications SM2S-QCS6490 SMARC Compute Module: CPU – 4x Arm Cortex-A78 (up to 2.7 GHz), 4x Arm Cortex-A55 (up to […]

Matter 1.3 specification adds support for water and energy management, electric vehicle chargers, and various household appliances

Matter 1.3 Specification

The Connectivity Standard Alliance (CSA) has just announced the release of the Matter 1.3 specification and SDK with energy reporting, support for water and energy management devices, electric vehicle chargers, several new “major appliances”, namely various kitchen appliances and laundry dryers, and various other features. As a reminder the Matter protocol was initially introduced several years ago under the name Project CHIP to improve the interoperability of Smart Home devices from various vendors, so for example, users could connect a Matter-compatible Philips Hue light bulb to a Samsung gateway, or a white-brand Matter sensor with Google Home, etc… Matter started to pick last year with several products launched, and Paisit notably reviewed the MINI Extreme Wi-Fi Smart Switch (MINIR4M), the first Matter device from SONOFF, last October. Matter 1.3 adds various new capabilities and devices. Matter 1.3 highlights: Support for Water and Energy Management Devices Energy Management – Matter 1.3 […]

MemryX MX3 edge AI accelerator delivers up to 5 TOPS, is offered in die, package, and M.2 and mPCIe modules

MemryX MX3 EVB

Jean-Luc noted the MemryX MX3 edge AI accelerator module while covering the DeGirum ORCA M.2 and USB Edge AI accelerators last month, so today, we’ll have a look at this AI chip and corresponding modules that run computer vision neural networks using common frameworks such as TensorFlow, TensorFlow Lite, ONNX, PyTorch, and Keras. MemryX MX3 Specifications MemryX hasn’t disclosed much performance stats about this chip. All we know is it offers more than 5 TFLOPs. The listed specifications include: Bfloat16 activations Batch = 1 Weights: 4, 8, and 16-bit ~10M parameters stored on-die Host interfaces – PCIe Gen 3 I/O and/or USB 2.0/3.x Power consumption – ~1.0W 1-click compilation for the MX-SDK when mapping neural networks that have multiple layers Under the hood, the MX3 features MemryX Compute Engines (MCE) which are tightly coupled with at-memory computing. This design creates a native, proprietary dataflow architecture that utilizes up to 70% […]

Sipeed MaixCAM is a RISC-V AI camera devkit with up to 5MP camera, 2.3-inch color touchscreen display, GPIOs

Sipeed MaixCAM

Sipeed MaixCAM is an AI camera based on SOPHGO SG2002 RISC-V (and Arm, and 8051) SoC with a 1 TOPS NPU that takes up to 5MP camera modules and comes with a 2.3-inch color touchscreen display. The development kit also comes with WiFi 6 and BLE 5.4 connectivity, optional Ethernet, audio input and output ports, a USB Type-C port, and two 14-pin GPIO headers for expansion that makes it suitable for a range of computer vision, Smart audio, and AIoT applications. Sipeed MaixCAM specifications: SoC – SOPHGO SG2002 CPU 1 GHz RISC-V C906 processor or Arm Cortex-A53 core (selectable at boot) running Linux 700 MHz RISC-V C906 core running an RTOS 25 to 300 MHz low-power 8051 processor NPU – 1 TOPS @ INT8 with support for models such as Mobilenetv2, YOLOv5, YOLOv8, etc… Video Codec – H.264, H.265, MJPEG hardware encoding and decoding up to 2K @ 30fps Memory […]

UP 7000 x86 SBC