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 is even suitable for true wireless stereo (TWS) headsets, and […]
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 to stack another HAT or bonnet on top, use a […]
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 to perform vision, voice, and vibration detection and recognition. It […]
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 to embedded platforms”. TVM that features a microcontroller backend, is […]
SolidRun already offers NXP based solutions with AI accelerators through products such as SolidRun i.MX 8M Mini SoM with Gyrfalcon Lightspeeur 2803S AI accelerator, or Janux GS31 Edge AI server with NXP LX2160A networking SoC, various i.MX 8M SoCs and up to 128 Gyrfalcon accelerators. All those solutions are based on one or more external Gyrfalcon AI chips, but earlier this year, NXP introduced i.MX 8M Plus SoC with a built-in 2.3 TOPS neural processing unit (NPU), and now SolidRun has just unveiled the SolidRun i.MX 8M Plus SoM with the processor together with development kits based on HummingBoard carrier boards. Specifications: SoC – NXP i.MX 8M Plus Dual or Quad with dual or quad-core Arm Cortex-A53 processor @1.6 GHz (industrial) / 1.8 GHz (commercial), with Arm Cortex-M7 up to 800MHz, Vivante GC7000UL 3G GPU (Vulkan, OpenGL ES 3.1, OpenCL 1.2), 2.3 TOPS NPU, 1080p60 H.264/H.265 video encoder, 1080p60 video decoder (H.265, H.264, VP9, VP8), Candence HiFi4 audio DSP System […]
Last July, we missed Qualcomm’s announcement of QCS410 and QCS610 processors designed to bring “premium camera technology, including powerful artificial intelligence and machine learning features formerly only available to high-end devices, into mid-tier camera segments”. The new SoC’s were recently brought to our attention by Lantronix as they have just introduced a new Open-Q 610 micro system-on-module (μSOM) based on Qualcomm QCS610 processor, as well as a development kit designed to bring such smart cameras to market. I first got a bit confused by the product name, but this goes without saying that it is completely unrelated to Qualcomm Snapdragon 610 announced over six years ago. Open-Q 610 micro system-on-module Open-Q 610 specifications: SoC – Qualcomm QCS610 CPU – Octa-core processor with 2x Kryo 460 Gold cores @ 2.2 GHz (Cortex-A76 class), and 6x Kryo 430 Silver low-power cores @ 1.8GHz (Cortex-A55 class) GPU – Qualcomm Adreno 612 GPU @ 845 MHz, with OpenGL ES 3.2, Vulkan 1.1, OpenCL 2.0 […]
Google Coral SBC was the first development board with Google Edge TPU. The AI accelerator was combined with an NXP i.MX 8M quad-core Arm Cortex-A53 processor and 1GB RAM to provide an all-in-all AI edge computing platform. It launched for $175, and now still retails for $160 which may not be affordable to students and hobbyists. Google announced a new model called Coral Dev Board Mini last January, and the good news is that the board is now available for pre-order for just under $100 on Seeed Studio with shipping scheduled to start by the end of the month. Coral Dev Board Mini specifications haven’t changed much since the original announcement, but we know a few more details: SoC – MediaTek MT8167S quad-core Arm Cortex-A35 processor @ 1.3 GHz with Imagination PowerVR GE8300 GPU AI/ML accelerator – Google Edge TPU coprocessor with up to 4 TOPS as part of Coral Accelerator Module System Memory – 2GB LPDDR3 RAM Storage – […]
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 Microphone – 2x MP34DT05 omnidirectional microphones Connectivity Ethernet version- 10/100M Ethernet RJ45 jack LoRa version – Same Murata CMWX1ZZABZ LoRa module as found on […]
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