artificial intelligence News - Page 79 of 119 - CNX Software

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, […]

December 9, 2020 by Abhishek Jadhav - No Comments

What is Federated Learning in 5G C-V2X?

Implementation approach of Federated learning

In the past, Bluetooth, Wi-Fi, 3G, 4G with onboard sensors and GPS gave a huge start to the partial or conditional automation of the vehicles. In today’s era of 4G/5G C-V2X (Cellular vehicle-to-everything) with onboard compute and sensors have pushed the connectivity evolution in the automotive industry. Work has increased on high-automation and fully autonomous vehicles. This is all possible due to wireless communication which is 5G. 5G brings high bandwidth, ultra-low latency, and high reliability to the board. With these promises, it becomes very evident that these technologies, specifically with 5G that can be leveraged in vehicles. The technology is not just to connect vehicles, but also tries to see if a vehicle can connect to a network to get some information from the cloud servers. For the use case of vehicles platooning, we can expect a latency of 10 ms with 99.99% reliability and a high data rate […]

December 7, 2020December 7, 2020 by Jean-Luc Aufranc (CNXSoft) - No Comments

AI Gateway combines Raspberry Pi Compute Module 4 with Google Coral Edge TPU

Techbase quickly unveiled an update to their Modberry industrial controllers with Modberry 500 CM4 DIN Rail computer as soon as Raspberry Pi Compute Module 4 was launched. The company has now introduced another Raspberry Pi CM4 based product with ModBerry AI GATEWAY 9500-CM4 gateway combining the Broadcom BCM2711C0 system-on-module to Google Edge TPU to cater to applications leveraging AI acceleration. ModBerry AI GATEWAY 9500-CM4 preliminary specifications: SoM – Raspberry Pi Compute Module 4 AI accelerator – Google Coral Edge TPU module with up to 4 TOPS connected over the RPi CM4’s PCIe interface Peripherals (customizable as needed) USB, HDMI, and Ethernet. Serial RS-232/485 ports Digital and analog I/Os with or without opto-isolation Relays, 1-Wire, CAN bus, M-Bus Master and Slave PCIe modules for wireless support Cellular – 4G/LTE and 5G modem, interchangeable with Coral TPU, plus on-board eSIM LPWAN – NB-IoT, LoRa, Sigfox, Wireless M-Bus ZigBee Additional Wi-Fi/Bluetooth interface or […]

December 3, 2020April 30, 2021 by Abhishek Jadhav - 2 Comments

Compact Jetson Xavier NX/ Nano open hardware baseboard supports Android

When it comes to NVIDIA Jetson family of modules, we should understand that NVIDIA Jetson Nano is for makers and STEM education, while Xavier NX is more geared towards professional and commercial use. The compute module Xavier NX was announced before the development kit, which includes the module and reference carrier board and otherwise for NVIDIA Jetson Nano. There are some third-party carrier boards & edge computers available for Jetson Xavier NX and Jetson Nano, including DesignCore Carrier Board and Diamond FLOYD Carrier Board. Antmicro, a custom hardware, software, and AI engineering company, came up with a compact open hardware device: Jetson Nano/ Xavier NX baseboard. It supports both NVIDIA Jetson Nano SoM as well as the Xavier NX SoM. The board is designed to put NVIDIA’s versatile module at the heart of cutting-edge projects and drive innovation in areas such as AI/ML, drones, medical and industrial object recognition systems. […]