computer vision

Posted on by Jean-Luc Aufranc (CNXSoft) - No Comments on UNIHIKER K10 low-cost TinyML education platform supports image detection and voice recognition

UNIHIKER K10 low-cost TinyML education platform supports image detection and voice recognition

UNIHIKER K10

UNIHIKER K10 is a low-cost STEM education platform for TinyML applications that leverages the ESP32-S3 wireless microcontroller with vector extensions for workloads such as image detection or voice recognition. The UNIHIKER K10 also features a built-in 2.8-inch color display, a camera, a speaker, a 2-microphone array, a few sensors, a microSD card, and a BBC Micro:bit-like edge connector for power signals and GPIOs. It’s a cost-optimized version of its Linux-based big brother – the UNIHIKER M10 – first unveiled in 2022. Arnon also reviewed the UNIHIKER in 2023, showing how to configure it, use the SIoT platform with MQTT message, and program it with Jupyter Notebook, Python, or Visual Studio Code. Let’s have a closer look at the new ESP32-S3 variant. UNIHIKER K10 specifications: Core module – ESP32-S3-WROOM-1 MCU – ESP32-S3N16R8 dual-core Tensilica LX7 up to 240 MHz with 512KB SRAM, 8MB PSRAM, 16MB flash Wireless – WiFi 4 and […]

Posted on by Debashis Das - 1 Comment on Renesas RZ/V2N low-power AI MPU integrates up to 15 TOPS AI power, Mali-C55 ISP, dual MIPI camera support

Renesas RZ/V2N low-power AI MPU integrates up to 15 TOPS AI power, Mali-C55 ISP, dual MIPI camera support

Renesas RZ V2N MPU

Renesas has recently introduced the RZ/V2N low-power Arm Cortex-A55/M33 microprocessor designed for machine learning (ML) and computer vision applications. It features the company’s DRP-AI3 coprocessor, delivering up to 15 TOPS of INT8 “pruned” compute performance at 10 TOPS/W efficiency, making it a lower-power alternative to the RZ/V2H. Built for mid-range AI workloads, it includes four Arm Cortex-A55 cores (1.8GHz), a Cortex-M33 sub-CPU (200MHz), an optional 4K image signal processor, H.264/H.265 hardware codecs, an optional Mali-G31 GPU, and a dual-channel four-lane MIPI CSI-2 interface. The chip is around 38% smaller than the RZ/V2H MPU and operates without active cooling. The RZ/V2N is suitable for applications like endpoint vision AI, robotics, and industrial automation. Renesas RZ/V2N specifications CPU Application Processor – Quad-core Arm Cortex-A55 @ 1.8 GHz (0.9V) / 1.1 GHz (0.8V) L1 cache – 32KB I-cache (with parity) + 32KB D-cache (with ECC) per core L3 cache – 1MB (with ECC, […]

Posted on by Jean-Luc Aufranc (CNXSoft) - No Comments on Zant – An open-source Zig SDK for neural network deployment on microcontrollers

Zant – An open-source Zig SDK for neural network deployment on microcontrollers

Zant neural network deployment microcontrollers

Zant is an open-source, cross-platform SDK written in Zig and designed to simplify deploying Neural Networks (NN) on microcontrollers. It comprises a suite of tools to import, optimize, and deploy NNs to low-end hardware. The developers behind the project developed Zant (formerly known as Zig-ant) after noticing many microcontrollers lacked robust deep learning libraries, and made sure it would be on various platforms such as ARM Cortex-M or RISC-V microcontrollers, or even x86 targets. Contrary to platforms like Edge Impulse that focus on network creation, Zant is about deployment and outputs a static, highly optimized library ready to be integrated into any existing work stack. Zant highlights: Optimized Performance – Supports quantization, pruning, and hardware acceleration techniques such as SIMD and GPU offloading. Low memory footprint – Zant employs memory pooling, static allocation, and buffer optimization to work on resources-constrained targets. Ease of Integration: With a modular design, clear APIs, […]

Posted on by Jean-Luc Aufranc (CNXSoft) - 1 Comment on Edge video processing platform features NXP i.MX 8M Plus, i.MX 93, or i.MX 95 SoC, supports up to 23 camera types

Edge video processing platform features NXP i.MX 8M Plus, i.MX 93, or i.MX 95 SoC, supports up to 23 camera types

DAB Embedded AquaEdge video processing platform

DAB Embedded AquaEdge is a compact computer based on NXP i.MX 8M Plus, i.MX 93, or i.MX 95 SoC working as an edge video processing platform and supporting 23 types of vision cameras with resolution from VGA up to 12MP, and global/rolling shutter. The small edge computer features a gigabit Ethernet RJ45 jack with PoE to power the device. It is also equipped with a single GSML2 connector to connect a camera whose input can be processed by the built-in AI accelerator found in the selected NXP i.MX processors. Other external ports include a microSD card slot, a USB 3.0 Type-A port, and a mini HDMI port (for the NXP i.MX 8M Plus model only). DAB Embedded AquaEdge specifications: SoC / Memory / Storage options NXP i.MX 8M Plus CPU – Quad-core Cortex-A53 processor @ 1.8GH, Arm Cortex-M7 real-time core AI accelerator – 2.3 TOPS NPU VPU Encoder up to […]

Posted on by Jean-Luc Aufranc (CNXSoft) - 2 Comments on YOLO-Jevois leverages YOLO-World to enable open-vocabulary object detection at runtime, no dataset or training needed

YOLO-Jevois leverages YOLO-World to enable open-vocabulary object detection at runtime, no dataset or training needed

YOLO-Jevois general object detection by typing words

YOLO is one of the most popular edge AI computer vision models that detects multiple objects and works out of the box for the objects for which it has been trained on. But adding another object would typically involve a lot of work as you’d need to collect a dataset, manually annotate the objects you want to detect, train the network, and then possibly quantize it for edge deployment on an AI accelerator. This is basically true for all computer vision models, and we’ve already seen Edge Impulse facilitate the annotation process using GPT-4o and NVIDIA TAO to train TinyML models for microcontrollers. However, researchers at jevois.org have managed to do something even more impressive with YOLO-Jevois “open-vocabulary object detection”, based on Tencent AI Lab’s YOLO-World, to add new objects in YOLO at runtime by simply typing words or selecting part of the image. It also updates class definitions on […]

Posted on by Jean-Luc Aufranc (CNXSoft) - 2 Comments on M5Stack LLM630 Compute Kit features Axera AX630C Edge AI SoC for on-device LLM and computer vision processing

M5Stack LLM630 Compute Kit features Axera AX630C Edge AI SoC for on-device LLM and computer vision processing

M5Stack LLM630 Compute Kit

M5Stack LLM630 Compute Kit is an Edge AI development platform powered by Axera Tech AX630C AI SoC with a 3.2 TOPS NPU designed to run computer vision (CV) and large language model (LLM) tasks at the edge, in other words, on the device itself without access to the cloud. The LLM630 Compute Kit is also equipped with 4GB LPDDR4 and 32GB eMMC flash and supports both wired and wireless connectivity thanks to a JL2101-N040C Gigabit Ethernet chip and an ESP32-C6 module for 2.4GHz WiFi 6 connectivity. You can also connect a display and a camera through MIPI DSI and CSI connectors. M5Stack LLM630 Compute Kit specifications: SoC – Axera Tech (Aixin in China) AX630C CPU – Dual-core Arm Cortex-A53 @ 1.2 GHz; 32KB I-Cache, 32KB D-Cache, 256KB L2 Cache NPU – 12.8 TOPS @ INT4 (max), 3.2 TOPS @ INT8 ISP – 4K @ 30fps Video – Encoding: 4K; Decoding:1080p […]

Posted on by Jean-Luc Aufranc (CNXSoft) - 2 Comments on NanoCOM-ADN – A COM Express Type 10 Module based on Intel Alder Lake-N SoC

NanoCOM-ADN – A COM Express Type 10 Module based on Intel Alder Lake-N SoC

AAEON NanoCOM-ADN COM Express module

AAEON NanoCOM-ADN is a compact (84 x 55mm) COM Express Type 10 module based on Intel Alder Lake-N SoCs, namely Processor N50/N97, Atom x7425E, or Core i3-N305 that adds another option to the SMARC, Qseven, and COM Express Type 6 modules offered by the company with the same processors. The NanoCOM-ADN computer-on-module features up to 16GB LPDDR5x soldered-on memory, up to 64GB eMMC flash, and two Intel i226-V 2.5GbE controllers. Its standard 220-pin COM Express board-to-board connector exposes two SATA interfaces, four PCIe Gen3 x1 interfaces, DDI and LVDS video outputs, eight USB interfaces, and more. AAEON explains the Alder Lale-N COM Express Type 10 CPU module is especially suited to digital signage, healthcare imaging, machine vision, industrial, robotics, and edge computing applications.   AAEON NanoCOM-ADN specifications: Alder Lake N-series SoC (one or the other) Intel Atom x7425E quad-core processor up to 3.4 GHz with 6MB cache, 24EU Intel UHD […]

Posted on by Jean-Luc Aufranc (CNXSoft) - 3 Comments on Raspberry Pi 5 edge AI computer ships with 8GB RAM, Hailo-8 AI module, supports Frigate NVR

Raspberry Pi 5 edge AI computer ships with 8GB RAM, Hailo-8 AI module, supports Frigate NVR

Raspberry Pi 5 Edge AI computer

Seeed Studio’s reComputer AI R2130-12 is an Edge AI computer with a Raspberry Pi 5 SBC with 8GB RAM and a Hailo-8 module with 26 TOPS of AI performance, suitable for video analytics, machine vision, and intelligent edge computing. The computer comes with a HAT+ with two M.2 sockets, one occupied by the Hailo-8 AI accelerator and the other available for an M.2 NVMe SSD. In some ways, it’s just a nicely packaged Raspberry Pi 5 SBC with Hailo-8 AI accelerator that we reviewed last year. reComputer AI R2130-12 specifications: SBC – Raspberry Pi 5 with 8GB RAM AI Processor – Hailo-8 M.2 AI accelerator module with 26 TOPS Storage microSD card slot, with support for high-speed SDR104 mode Optional M.2 NVMe SSD via M.2 PCIe 3.0 slot Video Output – 2x micro HDMI ports up to 4Kp60 Camera/Display I/F – 2x 4-lane MIPI camera/display transceivers Networking Gigabit Ethernet RJ45 […]

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