NXP i.MX 95 SMARC 2.1 system-on-modules – ADLINK LEC-IMX95 and iWave iW-RainboW-G61M

SMARC 2.1 development board NXP i.MX95

Several companies have unveiled SMARC 2.1 compliant system-on-modules powered by the NXP i.MX 95 AI SoC, and today we’ll look at the ADLINK LEC-IMX95 and iWave Systems iW-RainboW-G61M and related development/evaluation kits. The NXP i.MX 95 SoC was first unveiled at CES 2023 with up to six Cortex-A55 application cores, a Cortex-M33 real-time core, and a low-power Cortex-M7 core, as well as an eIQ Neutron NPU for machine learning applications. Since then a few companies have unveiled evaluation kits and system-on-modules such as the Toradex Titan evaluation kit or the Variscite DART-MX95 SoM, but none of those were compliant with a SoM standard, but at least two SMARC 2.1 system-on-modules equipped with the NXP i.MX 95 processor have been introduced. ADLINK LEC-IMX95 Specifications: SoC – NXP i.MX 95 CPU Up to 6x Arm Cortex-A55 application cores clocked at 2.0 GHz with 32K I-cache and D-cache, 64KB L2 cache, and 512KB […]

Rockchip RK2118G/RK2118M dual-core Star-SE Armv8-M microcontrollers target smart audio applications

Rockchip RK2118G microcontroller block diagram

Rockchip RK2118G and RK2118M smart audio microcontrollers based on a dual-core Star-SE Armv8-M processor, an NPU for smart AI audio processor, three DSPs, 1024KB SRAM, optional DDR memory in package, and a range of peripherals. I first noticed the RK2118M in slides from the Rockchip Developer Conference 2024 last March, but I did not have enough information for an article at the time. Things have now changed since I’ve just received a bunch of datasheets including the one for the RK2118G and RK2118G microcontrollers, which look identical except for the DDR interface and optional built-in 64MB RAM for the RK2118G. The datasheets have only one reference to Arm with the string “Arm-V8M” and nothing else, and Cortex is not mentioned at all. But the slide above reveals the STAR-SE core looks to be an Arm Cortex-M33 core. We also learn the top frequencies for the “STAR-M33″/”STAR-SE” core  (300MHz) and the […]

New NXP i.MX 93-based system-on-modules launched by MYiR, Variscite, and Compulab

MYIR MYD-LMX9X development board

We have covered announcements about early NXP i.MX 93-based system-on-modules such as the ADLINK OSM-IMX93 and Ka-Ro Electronics’ QS93, as well as products integrating the higher-end NXP i.MX 95 processor such as the Toradex Titan Evaluation kit. Three additional NXP i.MX 93 SoMs from Variscite, Dart, and Compulab are now available. Targeted at industrial, IoT, and automotive applications, the NXP i.MX 93 features a 64-bit dual-core Arm Cortex-A55 application processor running at up to 1.7GHz and a Cortex-M33 co-processor running at up to 250MHz. It integrates an Arm Ethos-U65 microNPU, providing up to 0.5TOPS of computing power, and supports EdgeLock secure enclave, NXP’s hardware-based security subsystem. The heterogeneous multicore processing architecture allows the device to run Linux on the main core and a real-time operating system on the Cortex-M33 core. The processor is designed for cost-effective and energy-efficient machine learning applications. It supports LVDS, MIPI-DS, and parallel RGB display protocols […]

BitNetMCU project enables Machine Learning on CH32V003 RISC-V MCU

Neural networks on the CH32V003

Neural networks and other machine learning processes are often associated with powerful processors and GPUs. However, as we’ve seen on the page, AI is also moving to the very edge, and the BitNetMCU open-source project further showcases that it is possible to run low-bit quantized neural networks on low-end RISC-V microcontrollers such as the inexpensive CH32V003. As a reminder, the CH32V003 is based on the QingKe 32-bit RISC-V2A processor, which supports two levels of interrupt nesting. It is a compact, low-power, general-purpose 48MHz microcontroller that has 2KB SRAM with 16KB flash. The chip comes in a TSSOP20, QFN20, SOP16, or SOP8 package. To run machine learning on the CH32V003 microcontroller, the BitNetMCU project does Quantization Aware Training (QAT) and fine-tunes the inference code and model structure, which makes it possible to surpass 99% test accuracy on a 16×16 MNIST dataset without using any multiplication instructions. This performance is impressive, considering […]

Synaptics Astra platform takes SL1620, SL1640, or SL1680 Arm CPU module for Edge AI applications

Astra Machina Foundation Series Dev Board

Synaptics has unveiled its new Astra platform with a range of SoC and a development kit for edge AI applications. These new processors and a supporting development kit are built to provide out-of-the-box AI capabilities for IoT devices, reducing reliance on cloud-based AI. This new Synaptics Astra Platform is built around three main SoCs. The SL1680 is built for multi-modal IoT applications and features a quad-core Arm Cortex-A73 CPU, dedicated 7.9 TOPS NPU, and 4K video. The SL1640 is a cost and power-optimized SoC with a quad-core Cortex-A55 CPU and 1.6+ TOPS NPU. Finally, the SL1620 is a graphics and AI accelerator with a quad-core Cortex-A55 CPU and dual-core Imagination BXE-2-32 GPU but does not feature an NPU. The development kit features a module design where the new swappable compute modules allow flexible configurations. The devkit will support open Yocto Linux distribution and Synaptics AI toolkit for quick AI integration. […]

STMicro LSM6DSV32X Edge AI motion sensor aims to extend battery life in wearables, trackers, and activity monitors

STMicro LSM6DSV32X AI motion sensor

STMicro LSM6DSV32X is a new low-power 6-axis inertial module with the company’s machine-learning core (MLC) with AI algorithms based on decision trees, a 3-axis accelerometer with a full-scale range of 32g, and a 4000 degrees-per-second gyroscope designed to measure intensive movements, impacts, and freefall height (estimation). The features of the sensors enable it to lower the power consumption for functions such as gym-activity recognition to under 6µA, while 3D orientation tracking consumes just 30µA when using STMicro’s Sensor Fusion Low-Power (SFLP) algorithm. It will be found in a range of battery-powered devices such as consumer wearables, asset trackers, and impact and fall alarms for workers or the elderly. STMicro LSM6DSV32X specifications: Triple-channel architecture for UI (user interface), EIS, and OIS data processing Data storage – Smart FIFO up to 4.5 KB Sensors Accelerometer – ±4/±8/±16/±32 g full scale Gyroscope – ±125/±250/±500/±1000/±2000/±4000 dps full scale Embedded temperature sensor Host interfaces SPI, […]

Infineon PSOC Edge E81, E83, E84 Cortex-M55/M33 MCUs target Machine Learning-enhanced IoT, consumer and industrial applications

PSOC Edge E84 microcontroller

Infineon PSOC Edge E81, E83, and E84 MCU series are dual-core Cortex-M55/M33 microcontrollers with optional Arm Ethos U55 microNPU and 2.5D GPU designed for IoT, consumer, and industrial applications that could benefit from machine learning acceleration. This is a follow-up to the utterly useless announcement by Infineon about PSoC Edge Cortex-M55/M33 microcontrollers in December 2023 with the new announcement introducing actual parts that people may use in their design. The PSOC Edge E81 series is an entry-level ML microcontroller, the PSOC Edge E83 series adds more advanced machine learning with the Ethos-U55 microNPU, and the PSOC Edge E84 series further adds a 2.5D GPU for HMI applications. Infineon PSOC Edge E81, E83, E84-series specifications: MCU cores Arm Cortex-M55 high-performance CPU system up to 400 Mhz with FPU, MPU, Arm Helium support, 256KB i-TCM, 256KB D-TCM, 4MB SRAM (Edge E81/E83) or 5MB SRAM (Edge E84) Arm Cortex-M33 low-power CPU system up […]

Octavo OSD32MP2 System-in-Package (SiP) packs an STM32MP25 SoC, DDR4, EEPROM, and passive components into a single chip

Octava Systems OSD32MP2 SiP

Octavo Systems OSD32MP2 is a family of two System-in-Package (SiP) modules, comprised of the OSD32MP2 and OSD32MP2-PM, based on the STMicro STM32MP25 Arm Cortex-A35/M33 AI processor, DDR4 memory, and various components to reduce the complexity, size, and total cost of ownership of solutions based on the STM32MP2 chips. The OSD32MP2 is a larger, yet still compact, 21x21mm package with the STM32MP25, DDR4, EEPROM, oscillators, PMIC, passive components, and an optional RTC, while the OSD32MP2-PM is even smaller at 14x9mm and combines the STM32MP25, DDR4, and passive components in a single chip. OSD32MP2 specifications: SoC – STMicro STM32MP25 CPU – Up to 2x 64-bit Arm Cortex-A35 @ 1.5 GHz MCU 1x Cortex-M33 @ 400 MHz with FPU/MPU; 1x Cortex M0+ @ 200 MHz in SmartRun domain GPU – VeriSilicon 3D GPU @ 900 MHz with OpenGL ES 3.2 and Vulkan 1.2 APIs support VPU – 1080p60 H.264, VP8 video decoder/encoder Neural […]