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Posted on by Jean-Luc Aufranc (CNXSoft) - 1 Comment on Quarky Intellio – A LEGO-compatible AI, Augmented Reality, and IoT learning platform (Crowdfunding)

Quarky Intellio – A LEGO-compatible AI, Augmented Reality, and IoT learning platform (Crowdfunding)

Quarky Intellio rover car robot

Quarky Intellio is an ESP32-S3-based development kit designed as an educational platform for learning AI, Augmented Reality (AR), and IoT concepts. It is compatible with LEGO bricks and targets users aged 10 and older. The core AI-AR module features an SPI TFT display interface, a 5MP camera, a speaker and microphone, a microSD card slot for data storage, a USB-C port for programming and charging, servo and GPIO expansion ports, and a 1,000 mAh battery. The company offers a discovery kit with the core module only as well as a rover car kit, and since it’s compatible with LEGO, users can easily create their own robot. Quarky Intellio specifications: Core module – Espressif Systems ESP32-S3-WROOM-1-N16R8 SoC – ESP32-S3 CPU – Dual-core LX7 processor with up to 240MHz Memory – 512KB SRAM, 8MB PSRAM (SiP) Storage – 384KB ROM Wireless – WiFi 4 802.11b/g/n and Bluetooth 5.0 LE Storage – 16MB flash […]

Posted on by Jean-Luc Aufranc (CNXSoft) - No Comments on Brainchip AKD1500 PCIe/SPI Edge AI co-processor to power battery-operated wearables and IoT devices

Brainchip AKD1500 PCIe/SPI Edge AI co-processor to power battery-operated wearables and IoT devices

BrainChip AKD1500 Edge AI co-processor with PCIe and SPI host interfaces

BrainChip AKD1500 is an ultra-efficient Edge AI co-processor delivering up to 800 GOPS while consuming just 300 mW of power, making it suitable for battery-powered wearables and IoT sensors. It’s based on the same event-based neuromorphic technology found on the earlier AKD1000, relying on spiking neural networks (SNN) to deliver real-time inference in a way that is much more efficient than traditional AI chips. What’s new here is that thanks to PCIe and SPI interfaces, the new ADK1500 co-processor can be paired to a wide range of hosts, ranging from Linux-capable application processors to resource-constrained microcontrollers with x86, Arm, or RISC-V architectures. BrainChip AKD1500 key features and specifications: Akida Neuron Fabric Clocked at 5 to 400 MHz Delivering up to 800 effective GOPS at <1mW/GOPS On-device learning capabilities to enable “secure application personalization, without the need for a Cloud connection or retraining” On-Chip Conversion Complex Memory/Storage 1MB on-chip local memory […]

Posted on by Debashis Das - No Comments on Firefly EC-AGXOrin – Jetson AGX Orin 64GB AI inference system supports up to 8 GMSL2 cameras

Firefly EC-AGXOrin – Jetson AGX Orin 64GB AI inference system supports up to 8 GMSL2 cameras

EC AGXOrin NVIDIA Jetson 275 TOPS Edge AI Computer

Firefly EC-AGXOrin is an NVIDIA Jetson AGX Orin 64GB-powered AI inference system, similar to the AAEON BOXER-8645AI and Vecow RAC-1000 rugged Edge AI systems, and designed for edge AI applications such as in-vehicle computing, robotic control, machine vision, intelligent video analytics, and mobile robots. The device features eight GMSL2 connectors (input via two 4-pin Mini FAKRA interfaces) and supports up to 22-channel 1080p or 8K, 30fps H.265 video decoding. It delivers 275 TOPS of AI performance through the NVIDIA module, and integrates 64GB LPDDR5 RAM, 64GB eMMC storage, and offers M.2 NVMe and MicroSD card storage options. Other ports include a 10GbE RJ45 jack, five GbE jacks, USB 3.0, HDMI 2.0, RS232, RS485, CAN, and the system also supports WiFi 6, Bluetooth 5.2, 4G/5G cellular connectivity, and GPS/GNSS. Firefly EC-AGXOrin specifications: SoM – Jetson AGX Orin module with CPU – 12-core Arm Cortex-A78AE v8.2 64-bit processor with 3MB L2 + […]

Posted on by Jean-Luc Aufranc (CNXSoft) - 3 Comments on Google’s open-source, RISC-V-based Coral NPU is integrated into Synaptics SL2610 Edge AI SoCs

Google’s open-source, RISC-V-based Coral NPU is integrated into Synaptics SL2610 Edge AI SoCs

Google Coral NPU

Google has very recently introduced Coral NPU full-stack, open-source RISC-V-based platform for always-on AI on low-power edge devices and wearables. The first chip to integrate the Coral NPU is the upcoming Synaptics Astra SL2610 family. Google Coral NPU The Coral NPU aims to address the software fragmentation on entry-level AI accelerators that makes them difficult to program. By releasing an open-source NPU and associated source code, Google hopes its design will be adopted by silicon vendors, reduce software fragmentation over time, and help machine learning (ML) developers bring products to market faster. Building on the works on the Coral platform, the new, open-source Coral NPU is comprised of three main components: A scalar core – A lightweight, C-programmable RISC-V core that manages data flow to the back-end cores. It uses a simple “run-to-completion” model for ultra-low power consumption and traditional CPU functions. A vector execution unit – A single instruction […]

Posted on by Debashis Das - 7 Comments on Raspberry Pi-like Allwinner A527/T527 industrial SBC features dual camera support and AI acceleration

Raspberry Pi-like Allwinner A527/T527 industrial SBC features dual camera support and AI acceleration

CDEBYTE ECB32 Series Multi core Heterogeneous Allwinner T527 SBC

EBYTE has recently released an Allwinner A527/T527-based Raspberry Pi-like industrial SBC with dual camera and AI features, with a design very similar to the Walnut Pi 2B, and to a lesser extent, the Orange Pi 4A. It is designed for embedded, IoT, and smart commercial applications. The board supports up to 4GB LPDDR4 RAM, 32GB eMMC storage, and MicroSD card expansion. Display options include HDMI 2.0 and a 4-lane MIPI DSI interface enabling dual 4K output, while two 4-lane MIPI CSI connectors allow simultaneous camera inputs. Connectivity options include Gigabit Ethernet, Wi-Fi 4, Bluetooth 4.2, and a PCIe 2.1 x1 slot for high-speed expansion. It also features four USB 2.0 ports, a Type-C power interface, and a 40-pin Raspberry Pi-compatible GPIO header. EBYTE Allwinner A527/T527 SBC specifications: SoC – Allwinner T527 (industrial) / Allwinner A527 (commercial) CPU Octa-core Arm Cortex-A55 processor with four cores @ 1.80 GHz (T527) or 2.0 […]

Posted on by Jean-Luc Aufranc (CNXSoft) - 2 Comments on Fogwise AIRbox Q900 – $599 Qualcomm IQ-9075 AI Box delivers up to 200 TOPS of AI performance

Fogwise AIRbox Q900 – $599 Qualcomm IQ-9075 AI Box delivers up to 200 TOPS of AI performance

Radxa Fogwise AIRbox Q900

Fogwise AIRBox Q900 AI box is an upgrade to the Fogwise Airbox powered by a Qualcomm IQ-9075 SoC with up to 200 TOPS (sparse) of AI performance, 36GB RAM, and 128GB UFS storage. Radxa says its new AI micro-server competes directly against the NVIDIA Jetson Orin NX 16GB, offering cheaper overall system cost, similar performance, and higher efficiency.  Other benefits include Cortex-R52 real-time cores, 2.5GbE networking, and separate GPU, NPU, and DSP. Fogwise AIRBox Q900 specifications: SoC – Qualcomm DragonWing IQ-9075 CPU Octa-core Kryo Gen 6 (Cortex-A78C-based) application cores @ up to 2.36 GHz Quad-ore Cortex-R52 real-time cores @ up to 1.85GHz GPU – Adreno 663 GPU delivering up to 1.2 TFLOPS FP32 with secure GPU compute; supports Vulkan 1.2, OpenGL ES 3.2, OpenCL 2.0 FP, Adreno NN Direct VPU – Adreno VPU 765 Video Decode AV1 / HEVC / H.265 / H.264 / VP9 / MPEG-2 1x 8Kp60 / […]

Posted on by Debashis Das - No Comments on Ambiq Apollo510B ultra-low-power Cortex-M55 Edge AI MCU adds Bluetooth LE 5.4

Ambiq Apollo510B ultra-low-power Cortex-M55 Edge AI MCU adds Bluetooth LE 5.4

Ambiq Apollo510B

After the release of Apollo510, Ambiq has released Apollo510B, an ultra-low power Edge AI MCU that adds a 48 MHz network coprocessor for Bluetooth 5.4 LE (BLE) support. The new SoC combines Cortex-M55 with Helium MVE for AI/ML acceleration, secureSPOT 3.0 security, and graphiqSPOT 2.0 graphics for connected wearables, healthcare devices, and industrial IoT applications. The Apollo510B features 3.75MB of system RAM, 4MB of non-volatile memory, and a 12-bit ADC. It supports MIPI DSI and Quad SPI interfaces for displays, and offers audio capabilities such as always-on low-power ADC, a PDM stereo microphone interface, and dual multichannel I²S ports with asynchronous sample rate conversion. Peripheral options extend to USB 2.0 HS/FS, dual SDIO/eMMC controllers, multiple SPI and I²C masters, UART interfaces with flow control, and various GPIOs. Ambiq Apollo510B specifications: MCU Core Arm Cortex-M55 core at up to 250 MHz with Helium (MVE) vector instructions, FPU, TrustZone, MPU Caches/TCM – […]

Posted on by Jean-Luc Aufranc (CNXSoft) - 30 Comments on Pi Zero-sized Radxa Cubie A7Z SBC features Allwinner A733 Cortex-A76/A55 SoC, up to 16GB RAM, WiFi 6

Pi Zero-sized Radxa Cubie A7Z SBC features Allwinner A733 Cortex-A76/A55 SoC, up to 16GB RAM, WiFi 6

Allwinner A733 Raspberry Pi Zero SBC

Radxa Cubie A7Z is the little brother of the Cubie A7A SBC, still based on a powerful Allwinner A733 octa-core Arm Cortex-A76/A55 SoC, but offered in a more compact form factor inspired by the Raspberry Pi Zero. The compact single board computer also comes with up to 16GB RAM, a microSD card slot, optional UFS flash, micro HDMI and USB-C DisplayPort video output, a WiFi 6 and Bluetooth 5.x wireless module, a 4-lane MIPI CSI camera connector, a PCIe Gen3 FPC connector (at first for Pi Zero-sized board), and a 40-pin GPIO header. Radxa Cubie A7Z specifications: SoC – Allwinner A733 (A733MX‑HN3) CPU Dual-core Arm Cortex-A76 @ up to 2.00 GHz Hexa-core Arm Cortex-A55 @ up to 1.79 GHz Single-core RISC-V E902 real-time core GPU – Imagination Technologies BXM-4-64 MC1 GPU with support for OpenGL ES 3.2, Vulkan 1.3, OpenCL 3.0 VPU 8Kp24 H.265/VP9/AVS2 decoding 4Kp30 H.265/H.264 encoding AI accelerator […]

Banana Pi BPI-R4 Pro networking SBC