artificial intelligence News - Page 73 of 114 - CNX Software

January 5, 2021October 24, 2024 by Saumitra Jagdale - 1 Comment

BHI260AP is an AI smart sensor with built-in IMU by Bosch Sensortec

BHI260AP AI smart sensor integrates a 6-axis IMU, a 32-bit customizable programmable microcontroller, and various software functionalities. The AI smart sensor has embedded AI with on-sensor applications such as fitness tracking, navigation, machine learning analytics, and orientation estimation. The dimensions of the miniaturized AI smart sensor are 4.1mm x 3.6mm x 0.83 mm. Hardware Features of BHI260AP AI Smart Sensor ARC EM4 CPU includes ARCv2 16/32 bit instruction set working up to a frequency of 3.6 MHz. The core also integrates Floating Point Unit (FPU) and Memory Protection Unit (MPU) with 4 channel micro DMA controller. CPU has two modes of AI functioning at 25Hz and 50Hz with 249µA and 386µA of current consumption respectively. Integrated sensor (6-DoF IMU) includes 16-bit 3 axis accelerometer and 16-bit 3 axis gyroscope. The sensor works at an operating voltage of 1.8 V with a standby current value of 8µA, hence the power consumption […]

January 2, 2021January 3, 2021 by Jean-Luc Aufranc (CNXSoft) - 9 Comments

ESP32-S3 dual-core WiFi and Bluetooth LE 5 SoC supports AI acceleration for AIoT applications

Back in September, we reported that Espressif Systems planned to release a new ESP32-S3 with “AI instructions and multi-CPU cores” with few other details, except the chip would also be part of the MINI-series wireless modules. Now we have many more details, as the Shanghai-based company has now officially announced ESP32-S3. The processor features dual Tensilica LX7 cores, 2.4 GHz WiFi 4 & Bluetooth 5 connectivity, and as expected supports AI instructions to cater to the AIoT (AI + IoT) market. ESP32-S3 key features and specifications: CPU Dual-core Tensilica LX7 up to 240 MHz with additional vector instructions for AI acceleration ULP core to handle low power modes Memory – 512 KB of internal SRAM Storage – Octal SPI flash and PSRAM support (supports larger, high-speed devices compared to ESP32) Cache – Connectivity 2.4 GHz 802.11 b/g/n Wi-Fi 4 with 40 MHz bandwidth support Bluetooth Low Energy (BLE) 5.0 connectivity […]

December 30, 2020December 30, 2020 by Jean-Luc Aufranc (CNXSoft) - 2 Comments

Pumpkin i500 SBC uses MediaTek i500 AIoT SoC for computer vision and AI Edge computing

MediaTek Rich IoT SDK v20.0 was released at the beginning of the year together with the announcement of Pumpkin i500 SBC with very few details except it would be powered by MediaTek i500 octa-core Cortex-A73/A55 processor and designed to support computer vision and AI Edge Computing. Pumpkin i500 hardware evaluation kit was initially scheduled to launch in February 2020, but it took much longer, and Seeed Studio has only just listed the board for $299.00. We also now know the full specifications for Pumpkin i500 SBC: SoC – MediaTek i500 octa-core processor with four Arm Cortex-A73 cores at up to 2.0 GHz and four Cortex-A53 cores, an Arm Mali-G72 MP3 GPU, and dual-core Tensilica Vision P6 DSP/AI accelerator @ 525 MHz System Memory – 2GB LPDDR4 Storage – 16GB eMMC flash Display – 4-lane MIPI DSI connector Camera – Up to 25MP via MIPI CSI connector Video Decoding – 1080p60 […]

December 28, 2020December 28, 2020 by Saumitra Jagdale - 5 Comments

Tiny mmWave radar sensor embeds Cortex-R4F core for object tracking, classification

The 1″ cube mmWave RS-6843AOPUA radar sensor, announced by D3 Engineering, is a miniature radar device that enables easy integration of radar algorithms for industrial applications. The RS-6843AOPUA radar sensor is an AECQ-100-qualified 60 GHz device that integrates a Texas Instruments C674x DSP for algorithms and an Arm Cortex-R4F microcontroller unit for decision-making and interfacing. It also has a radar accelerator and an “on-package antenna array.” The RS-6843AOPUA radar sensor features a 1-inch cube form factor, heat-spreading metal body, mounting tabs, and a USB-Serial interface. The USB-Serial interface could be used to test and evaluate the radar sensor. Functions and Applications of the Radar Sensor TI C674x DSP: FMCW (Frequency Modulated Continuous Wave) signal processing, Implementation of algorithms Radar Accelerator: Radar data processing Arm Cortex-R4F MCU: Object tracking, Classification, Communications “The RF front end integrates a PLL, three transmitters, four receivers, and baseband ADC, and allows the sensor to cover […]

December 27, 2020December 26, 2020 by Saumitra Jagdale - 1 Comment

LG launches LG8111 AI SoC and development board for Edge AI processing

LG Electronics has designed LG8111 AI SoC for on-device AI inference and introduced the Eris Reference Board based on the processor. The chip supports hardware processing in artificial intelligence functions such as video, voice, and control intelligence. LG8111 AI development board is capable of implementing neural networks for deep learning specific algorithms due to its integrated “LG-Specific AI Processor.” Also, the low power and the low latency feature of the chip enhances its self-learning capacity. This enables the products with LG8111 AI chip to implement “On-Device AI.” Components and Features of the LG8111 AI SoC LG Neural engine, the AI accelerator has an extensive architecture for “On-Device” Inference/Leaning with its support on TensorFlow, TensorFlow Lite, and Caffe. The CPU of the board comes with four Arm Cortex A53 cores clocked at 1.0 GHz, with an L1 cache size of 32KB and an L2 cache size of 1MB. The CPU also […]

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 […]

December 11, 2020December 11, 2020 by Jean-Luc Aufranc (CNXSoft) - 6 Comments

Jetson Mate Cluster box takes four Jetson Nano/Xavier NX modules

While we’ve seen plenty of cluster boards based on Raspberry Pi SBC or Compute Modules, I had never seen clusters of Jetson modules. Those already exist, and PicoCluster has a few, but what made me write this post today is Seeed Studio’s Jetson Mate cluster box equipped with a carrier board taking up to four NVIDIA Jetson Nano or Xavier NX modules, an enclosure covered with a largish cooling fan with RGB LED for good effect… Jetson Mate specifications: SoM compatibility – Jetson Nano or Jetson Xavier NX via four SO-DIMM sockets Video Output – HDMI 2.0 Networking Gigabit Ethernet (RJ45) port Microchip KSZ9896CTXC 6-port GigE Managed Switch for internal networking between the modules and to the outside world Camera – 2x MIPI CSI connectors USB – 4x USB 3.0 ports (one per module), 1x USB 2.0 port, 1x USB-C port for power Debugging – UART debug pins (4x pairs, […]