Decoding Transformers on Edge Devices

This blog introduces the transformer encoder and decoder architecture and discusses the challenges in inferring them on an edge device: high computational cost, high peak memory requirements, low arithmetic intensity.

Cheap Computing and the Balancing Act of Population Decline

Imagine a world where computing power reaches a historic practical equivalent of two human brains. In this blog article by our Director of Systems Software, Cristian Olar explores how our revolutionary Metis AIPU achieves a remarkable 200 TOPS result at a fraction of today’s costs.

The Metis AI Platform in detail

The Metis AI Platform is a one-of-a-kind holistic hardware and software solution establishing best-in-class performance, efficiency, and ease of use for AI inferencing of computer vision workloads at the Edge.

Transformers in Computer Vision

Convolutional Neural Networks (CNN) have been dominant in Computer Vision applications for over a decade. Today, they are being outperformed and replaced by Vision Transformers (ViT) with a higher learning capacity. The fastest ViTs are essentially a CNN/Transformer hybrid, combining the best of both worlds.