RISC-V is inevitable – it became the mantra of RISC-V, and it’s true. But before we see why that is, let’s step back and discuss what RISC-V is and why we should care.
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.
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 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.
Our CTO and Co-Founder Evangelos Eleftheriou, presented at the ESSCIRC – ESSDERC 2022 event about In-memory computing for deep-learning acceleration.
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.
What’s Next for Data Processing? A Closer Look at In-Memory Computing Technology is progressing at an incredible pace and no technology is moving faster than Artificial Intelligence (AI). Indeed, we are on the cusp of an AI revolution which is already reshaping our lives.