Novel in-memory computing accelerators for Machine Learning (ML) on the edge

Research Partners

IMECIMEC
IBM Research IBM Research GmbH

Sources of Funding

Compusapien
WiPLASH H2020
Fvllmonti


Description

We explore a novel general-purpose CPU design that tightly integrates analog in-memory computing accelerators in processor pipelines execution pipelines. The accelerator is able to execute accelerating matrix-vector multiplications, which dominate deep neural network applications, in constant time, while presenting a high degree of flexibility, allowing to seamlessly accommodate diverse workloads.



Related Publications

Using Algorithmic Transformations and Sensitivity Analysis to Unleash Approximations in CNNs at the Edge
Ponzina, Flavio; Ansaloni, Giovanni; Peon Quiros, Miguel; Atienza Alonso, David
2022-07-19MDPI Micromachines - Special Issue "Hardware-Friendly Machine Learning and Its Applications"Publication funded by Compusapien (Next-gen computing systems inspired by the human brain)Publication funded by WiPLASH H2020 (New on-chip wireless communication plane)Publication funded by SNF ML-edge (ML-edge: Enabling Machine-Learning-Based Health Monitoring in Edge Sensors via Architectural Customization)Publication funded by Fvllmonti ((FETPROACT))
Error Resilient In-Memory Computing Architecture for CNN Inference on the Edge
Rios, Marco Antonio; Ponzina, Flavio; Ansaloni, Giovanni; Levisse, Alexandre Sébastien Julien; Atienza Alonso, David
2022-06-07Proceedings of the Great Lakes Symposium on VLSI 2022 Publication funded by Compusapien (Next-gen computing systems inspired by the human brain)Publication funded by WiPLASH H2020 (New on-chip wireless communication plane)Publication funded by SNF ML-edge (ML-edge: Enabling Machine-Learning-Based Health Monitoring in Edge Sensors via Architectural Customization)Publication funded by Fvllmonti ((FETPROACT))