Category: AI

There have been several recent developments around TinyML and edge AI applications, and to supply the increasing demand from the developer community, many embedded electronic device manufacturers around the world are designing AI development boards. Beijing-based online embedded...

Computer vision applications that range from object detection and pattern recognition to computational healthcare and security surveillance systems take the input image for further processing, which in traditional hardware implementation has a vision sensing and vision processing...

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...

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...

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...

The Dev Board Micro board combines a camera, microphone and Coral Edge TPU with an NXP i.MX microcontroller Google is launching an edge AI board in the same form factor as a Raspberry Pi Nano for its Coral AI processor and NCP’s i.MX microcontroller for the first time. The Coral Dev...

Recent breakthroughs in embedded machine learning have increased the demand for TinyML applications. In dealing with TinyML, there is an excellent platform to get started and build complex projects, Edge Impulse. We have also witnessed several new embedded devices coming to market...