machine learning News - Page 15 of 21 - CNX Software

March 23, 2021March 23, 2021 by Jean-Luc Aufranc (CNXSoft) - 1 Comment

Made in Thailand CorgiDude RISC-V AI board aims to teach machine learning

There’s a relatively small but active maker community in Thailand, and we’ve covered or even reviewed some made in Thailand boards including ESP8266 and ESP32 boards, a 3G Raspberry Pi HAT, and KidBright education platform among others. MakerAsia has developed CorgiDude, a board based on the version of Sipeed M1 RISC-V AI module with built-in WiFi, and part as a kit with a camera and a display used to teach machine learning and artificial intelligence with MicroPython or C/C++ programming. CorgiDude board specifications: AI Wireless Module – Sipeed M1W Module with Kendryte K210 dual-core 64-bit RISC-V RV64IMAFDC CPU @ 400Mhz with FPU, various AI accelerators (KPU, FFT accelerator…), 8MiB on-chip SRAM Espressif ESP8285 single-core 2.4 GHz WiFi 4 SoC plus IPEX antenna connector Storage – MicroSD card slot Camera I/F for 2MP OV2640 sensor up to 1280 × 1024 (SXGA) @ 30 fosm SVGA @ 30 fps, or CIF @ […]

March 16, 2021 by Jean-Luc Aufranc (CNXSoft) - No Comments

Sparkfun Thing Plus – Quicklogic EOS S3 Arm eFPGA board launched in Crowd Supply

SparkFun Electronics is a well-known electronics retailer that usually sells its in-house developed or third-party boards through its own online store. But this time around, the company decided to launch “Sparkfun Thing Plus – Quicklogic EOS S3” through Crowd Supply crowdfunding platform. The board is based on QuickFeather board designed with the same Quicklogic EOS S3 Arm Cortex-M4 plus embedded FPGA SoC, but follows Sparkfun’s Thing Plus form factor with a Qwiic connector and a different mix of sensors. Sparkfun Thing Plus – Quicklogic EOS S3 (QTPLUS-1.0) board specifications: SoC – QuickLogic EOS S3 MCU + eFPGA SoC with Arm Cortex-M4F Microcontroller up to 80 MHz, up to 512 Kb SRAM, and an embedded FPGA (eFPGA) with 2400 effective logic cells, 64 Kb RAM Storage – 16 Mbit SPI NOR flash (GigaDevice GD25Q16CEIGR) Sensors STMicro LIS2DH12TR accelerometer Digital pulse density modulation (PDM) microphone with Wake-on-Sound (WoS) feature: Vesper VM3011-U1 Expansion […]

March 12, 2021 by Saumitra Jagdale - 27 Comments

Machine Learning on Raspberry Pi Pico, RP2040, and future RPi MCUs

Although the Raspberry Pi Pico comes with the RP2040 chip that lacks the performance to implement machine learning inference for its applications. However, we saw a person detection use case through ArduCAM and TensorFlow lite interface. But, the processing performance of the use case was on the slower side. Additionally, a recent Eben Upton presentation also unveiled that due to low power requirements the board compensates the processing efficiency. Hence, it offers low-performance for edge inference and machine learning use cases. Eben Upton’s teaser on improvement in machine learning and the future scope of “Pi Silicon” revealed potential growth and development in edge inference applications. The demand for RP2040 boards has given rise to the market necessity for more boards. This demand can only be fulfilled if more boards with RP2040 chip are available in the market and company “partners such as Adafruit, Pimoroni, Adafruit and Sparkfun are start releasing […]

January 28, 2021May 13, 2021 by Jean-Luc Aufranc (CNXSoft) - 4 Comments

Arduino Portenta H7 Gets Embedded Vision Shield with Ethernet or LoRa Connectivity

[Update January 28, 2021: The LoRa version of Portenta Vision Shield is now available] 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 […]

January 11, 2021January 10, 2021 by Saumitra Jagdale - No Comments

DBM10 AI SoC is optimized for battery-powered voice and sensor processing

DSP Group announced DBM10 a low-power AI/ML-enabled dual-core SoC. The SoC is equipped with a DSP (Digital Signal Processor) and a dedicated nNetLite NN (Neural Network) processor that improves voice and sensor processing and ensures low-power consumption when working with sufficient-sized neural networks. Key Specifications of NN Processor Form factor: ~4 mm2 Ultra-low-power inference consumption – ~500 µW (typical) for voice NN algorithms Runs Hello Edge 30-word detection model @ 1 MHz (125 MHz available) Allows porting of large models (10s of megabytes) without significant accuracy loss using model optimization and compression. DBM10 AI SoC uses the combined functioning of machine learning, voice, and sensor parameters. This includes voice trigger (VT), voice authentication (VA), voice command (VC), noise reduction (NR), acoustic echo cancellation (AEC), sound event detection (SED), proximity and gesture detection, sensor data processing, and equalization. The DBM10 is suitable for battery-operated devices like smartphones, tablets, and wearables. It […]

January 6, 2021 by Saumitra Jagdale - 1 Comment

Adafruit Voice Bonnet is meant for DIY Raspberry Pi Smart Speakers

Adafruit Voice Bonnet features two speakers and two mics, that can be used as an audio-voice interface for Raspberry Pi SBC to create a DIY smart speaker or other audio product. The voice bonnet can work with any Raspberry Pi from Pi Zero up to Pi 4, with 40-pin 2 x 20 connector. Two speaker outputs of the voice bonnet have a power rating of 1 Watt. The voice bonnet contains 3.5 mm stereo outputs, headphone stereo, or line-out audio. The Adafruit voice bonnet has an on-board WM8960 low-power stereo codec that uses I2S digital audio for both input and output. The WM8960 codec has a dual analog input, it consists of a left mic and a right mic. The codec integrates a complete microphone interface and a stereo headphone driver. Adafruit says “For DIY speakers, solder any 1W+ speaker to one of these JST 2-PH cables. If you’d like […]

December 24, 2020October 24, 2024 by Saumitra Jagdale - 4 Comments

Himax WE-I Plus EVB AI development board supports TFLite for microcontrollers

Himax WE-I Plus EVB Endpoint AI Development Board

Himax WE-I Plus EVB is a low-power AI development board focused on machine learning and deep learning applications with its support for the TensorFlow Lite framework for Microcontrollers. It consists of majorly two significant components. First, HX6537-A ASIC is an ultra-low-power microcontroller designed for battery-powered TinyML applications. Second, HM0360 VGA mono camera with ultra-low power and CMOS image sensing features for CV(Computer Vision) based applications like object classification and recognition. The All in One AI Development Board The Development Board consists of HX6537-A ASIC, with built-in ARC EM9D DSP working at 400MHz frequency. It contains internal 2MB ultra-low leakage SRAMs for system and program usage. It also contains two LEDs to display classification results. Connections with external sensors/devices can be established using I2C and GPIOs interface present in its expansion header. “The all-in-one WE-I Plus EVB includes an AI processor, HM0360 AoS VGA camera, 2 microphones, and a 3-axis accelerometer […]

December 23, 2020December 23, 2020 by Abhishek Jadhav - No Comments

Use AutoTVM and uTVM to optimize ML workloads on embedded devices & microcontrollers

We are seeing a massive increase in resource-constraints for embedded devices due to a lack of mature software stacks. With the increase in open-source hardware, the available software support takes a considerable amount of time to develop AI/ML/DL applications. Some of the challenges faced today are that bare-metal devices do not have on-device memory management, and they do not have LLVM support. They are also hard to debug because of rigid programming and cross-compilation interfaces. Due to this, “optimizing and deploying machine learning workloads to bare-metal devices today is difficult”. To tackle these challenges, there have been developments to support TVM, an open-source machine learning compiler framework for CPUs, GPUs, and machine learning accelerators, on these bare-metal devices, and Apache TVM is running an open-source foundation to make this easy. “µTVM is a component of TVM that brings broad framework support, powerful compiler middleware, and flexible autotuning and compilation capabilities […]