PetaOps/W edge-AI μ Processors: Myth or reality?

With the rise of deep learning (DL), our world braces for artificial intelligence (AI) in every edge device, creating an urgent need for edge-AI SoCs. This SoC hardware needs to support high throughput, reliable and secure AI processing at ultra-low power (ULP), with a very short time to market. With its strong legacy in edge solutions and open...

SparseMEM: Energy-efficient Design for In-memory Sparse-based Graph Processing

Performing analysis on large graph datasets in an energy-efficient manner has posed a significant challenge; not only due to excessive data movements and poor locality, but also due to the non-optimal use of high sparsity of such datasets. The latter leads to a waste of resources as the computation is also performed on zero's operands which do...

Dependability of Future Edge-AI Processors: Pandora’s Box

This paper addresses one of the directions of the HORIZON EU CONVOLVE project being dependability of smart edge processors based on computation-in-memory and emerging memristor devices such as RRAM. It discusses how how this alternative computing paradigm will change the way we used to do manufacturing test. In addition, it describes how these...

Dealing with Non-Idealities in Memristor Based Computation-In-Memory Designs

Computation-In-Memory (CIM) using memristor devices provides an energy-efficient hardware implementation of arithmetic and logic operations for numerous applications, such as neuromorphic computing and database query. However, memristor-based CIM suffers from various non-idealities such as conductance drift, read disturb, wire parasitics,...