MYIR introduces i.MX 8M Plus module and devkit with AI/ML capabilities

MYIR i.MX 8M Plus development kit

There are already plenty of i.MX 8M Plus systems-on-module, but here’s one more courtesy of MYIR Tech with MYC-JX8MPQ i.MX 8M Plus module with as well as MYD-JX8MPQ development board for evaluating the solution. The module is especially well suited to applications leveraging Artificial Intelligence (AI) and Machine Learning (ML) with the NXP Cortex-A53/M7 integrating a 2.3 TOPS Neural Processing Unit (NPU). The module comes with up to 6GB LPDDR4, 128GB eMMC flash, 32MB QSPI flash, a PMIC for power management, as well as a 314-pin MXM 3.0 connector exposing the I/Os from the processor. MYC-JX8MPQ module specifications: SoC – NXP i.MX 8M Plus (MIMX8ML8CVNKZAB) quad-core Cortex-A53 processor @ 1.6 GHz, real-time Arm Cortex-M7 co-processor @ 800 MHz, 2.3 TOPS AI accelerator, 2D/3D GPU, HiFi4 Audio DSP, and 1080p VPU System Memory – 3GB LPDDR4 (option up to 6GB) Storage – 8GB eMMC flash (option up to 128GB), 32MB QSPI […]

StarFive Dubhe 64-bit RISC-V core to be found in 12nm, 2 GHz processors

StarFive Dubhe RISC-V Core

StarFive has just announced customers’ delivery of the 64-bit RISC-V Dubhe core based on RV64GC ISA plus bit manipulation, user-level interrupts, as well as the latest Vector 1.0 (V) and Hypervisor (H) instructions. StarFive Dubhe can be clocked up to 2 GHz on a 12nm TSMC process node, and the company also released performance numbers with a SPECint2006 score of 8.9/GHz, a Dhrystone score of 6.6 DMIPS/MHz, and a CoreMark score of 7.6/MHz. A third-party source told CNX Software it should be equivalent to the SiFive Performance P550 RISC-V core announced last summer, itself comparable to Cortex-A75. StarFive Dubhe highlights: Typical frequency – 2.0 GHz @ TSMC 12nm “Industry-leading” Power and Area Efficiency (TSMC 12nm) RISC-V Vector Extension Data types: floating point, fixed point and integer VLEN=128-1024bits ALU & data path width=128 or 256 bits Full vector register grouping (LMUL) support RISC-V Virtualization Extension Pre-integrated Multi-Core with Memory Coherency Support […]

Seeed XIAO BLE – A tiny nRF52840 Bluetooth 5.0 board with (optional) IMU sensor and microphone

Seeed XIAO BLE Sense

Seeed Studio has just introduced two new members to their XIAO board family with the Seeed XIAO BLE and XIAO BLE Sense boards equipped with Nordic Semi nRF52840 Bluetooth 5.0 microcontroller, as well as an IMU sensor and microphone on the “Sense” model. Just like the earlier XIAO RP2040 board, the tiny Seed XIAO BLE board can be programmed with Arduino, MicroPython, and CircuityPython, and offers two headers with 7-pin each for GPIOs. What’s really new is the wireless connectivity, the sensors, and a battery charging circuitry. Seeed XIAO BLE specifications: Wireless MCU –  Nordic nRF52840 Arm Cortex-M4F microcontroller @ up to 64 MHz with  1 MB flash, 256 KB SRAM, Bluetooth 5.0, NFC, Zigbee connectivity Storage – 2 MB QSPI flash Expansion I/Os 2x 7-pin headers with 1x UART, 1x I2C, 1x SPI, 1x NFC, 1x SWD, 11x GPIO (PWM), 6x ADC 3.3V I/O voltage (not 5V tolerant) Sensors […]

NXP i.MX 93 processor combines Cortex-A55 cores with Ethos U65 microNPU

NXP i.MX 93 (935x/933x)

NXP has unveiled the i.MX 93 processor family comprised of i.MX 935x, 933x, 932x, and 931x parts at this time with up to two Cortex-A55 cores, one Arm Cortex-M33 real-time core, as well as an Ethos U65 microNPU for machine learning (ML). We wrote about i.MX 9 family back in March with NXP telling us it would include an Arm Ethos U-65 microNPU and EdgeLock secure enclave, be manufactured with a 16/12nm FinFET class process, and includes the “Energy Flex” architecture to optimize power consumption by turning on/off specific blocks in the processor. The NXP i.MX 93 is the first family leveraging those new features, and we know have some more details. NXP i.MX 93 processor specifications: CPU 1x or 2x Arm Cortex-A55 @ 1.7 GHz with 32KB I-cache, 32KB D-cache, 64KB L2 cache, 256KB L3 cache with ECC 1x Arm Cortex-M33 @ 250 MHz low power microcontroller with 256KB […]

Cortex-M55 based Arm Virtual Hardware is now available in AWS Cloud

Arm Virtual Hardware Components

The Arm DevSummit 2021 is taking place on October 19-21, and the first announcements from Arm are related to IoT with “Arm Total Solutions for IoT delivering a full-stack solution to significantly accelerate IoT product development and improve product ROI”, “Project Centauri” aiming to achieve for an extensive Arm Cortex-M software ecosystem in the way that Project Cassini does for the Cortex-A ecosystem, starting with support for PSA Certified and Open-CMSIS-CDI cloud-to-device specification, and Arm Virtual Hardware based on Corstone-300 IoT platform with a Cortex-M55 MCU core and an Ethos-U55 microNPU accessible from Amazon Web Services. The first two are quite abstract right now, and more information may become available in the future, but the Arm Virtual Hardware is available now from AWS as a public beta, with 100 hours of free AWS EC2 CPU credits for the first 1,000 qualified users. The virtual hardware does not emulate only the […]

Raspberry Pi smart audio devkit features AISonic IA8201 DSP, microphone array

AISonic-Raspberry Pi Development Kit

Knowles AISonic IA8201 Raspberry Pi development kit is designed to bring voice, audio edge processing, and machine learning (ML) listening capabilities to various systems, and can be used to evaluate the company’s AISonic IA8201 DSP that was introduced about two years ago. The kit is comprised of three boards with an adapter board with three buttons connecting to the Raspberry Pi, as well as the AISonic IA8210 DSP board itself connected via a flat cable to a microphone array. Knowles AISonic Raspberry Pi development kit Knowles did not provide the full details for the development but says it enables wake-on-voice processing for low latency voice UI, noise reduction, context awareness, and accelerated machine learning inferencing for edge processing of sensor inputs. Some of the use cases include Low Power Voice Wake to listen for specific OEM keywords to wake the host processor, Proximity Detection when combined with an ultrasonic capable […]

AIfES for Arduino high-efficiency AI framework for microcontrollers becomes open source

AlFes for Arduino

AIfES (AI for Embedded Systems) is a standalone, high-efficiency, AI framework, which allows the Fraunhofer Institute for Microelectronic Circuits and Systems, or Fraunhofer IMS for short, to train and run machine learning algorithms on resource-constrained microcontrollers. So far the framework was closed-source and only used internally by Fraunhofer IMS, but following a collaboration with Arduino, AIfES for Arduino is now open-source and free to use for non-commercial projects. The framework has been optimized to allow 8-bit microcontrollers such as the one found in Arduino Uno to implement an Artificial Neural Network (ANN) that can be trained in moderate time. That means offline inference and training on small self-learning battery-powered devices is possible with AIfES without relying on the cloud or other devices. The library implements Feedforward Neural Networks (FNN) that can be freely parameterized, trained, modified, or reloaded at runtime. Programmed in C language, AIfES uses only standard libraries based […]

Benchmarking TinyML with MLPerf Tiny Inference Benchmark

MLPerf Tiny Inference Benchmark

As machine learning moves to microcontrollers, something referred to as TinyML, new tools are needed to compare different solutions. We’ve previously posted some Tensorflow Lite for Microcontroller benchmarks (for single board computers), but a benchmarking tool specifically designed for AI inference on resources-constrained embedded systems could prove to be useful for consistent results and cover a wider range of use cases. That’s exactly what MLCommons, an open engineering consortium, has done with MLPerf Tiny Inference benchmarks designed to measure how quickly a trained neural network can process new data for tiny, low-power devices, and it also includes an optional power measurement option. MLPerf Tiny v0.5, the first inference benchmark suite designed for embedded systems from the organization, consists of four benchmarks: Keyword Spotting – Small vocabulary keyword spotting using DS-CNN model. Typically used in smart earbuds and virtual assistants. Visual Wake Words – Binary image classification using MobileNet. In-home security […]

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