Author: Abhishek Jadhav

NVIDIA brought Jetson Xavier NX with supercomputer performance to edge locations delivering up to 21 TOPS to run neural networks in parallel and process data from several high-resolution external sensors. To  take advantage of the NVIDIA Jetson Xavier NX, many manufacturers developed...

Shenzhen Xunlong released its new board design Orange Pi 4 LTS, with almost the same specifications and layout as Orange Pi 4 but with minor changes. It is considered to be an optimized version of the original Orange Pi 4 integrated with readily available components while keeping the...

Today, famous Italian electronic embedded device manufacturer, Arduino launched an ultra-compact dual-core Nicla vision board for computer vision applications deployed at the edge. The minimalistic design gets a 2MP color camera that will aid in image detection, facial recognition,...

We have already come across tens if not hundreds of Raspberry Pi’s in-house silicon tapeout RP2040 integrated boards. The RP2040 was first seen in Raspberry Pi’s own microcontroller board– RPi Pico that features a dual-core Arm Cortex-M0+ processor with 264KB internal RAM and...

Last week, Codasip, known for edge tools and IPs, has announced RISC-V-based embedded cores for AI/ML edge customizations – L31 and L11 RISC-V processor cores. In addition to the existing low-power embedded cores, the L31 and L11 are aimed towards easing the customization process...

Even after the release of a 64-bit Raspberry Pi single-board computer, the Raspberry Pi OS remained with the 32-bit version– but in the making of a 64-bit OS. After a series of testing and validation from in-house engineers, while taking feedback from the community after the beta...

AI accelerators are specialized hardware designs that are built for computing complex AI workloads in the field of edge computing. While deep neural networks are assumed to be the optimized solution for image recognition and object detection, AI tasks, a group of researchers from the...

Aspinity's analogML core features the improved capabilities of a tinyML chip with low-power analog neuromorphic computing architecture– with a system-level approach to low-power edge processing. Without making use of power-hungry digitization and digital processors, the analogML core...