1.1k citations · 1.1k across the 5 of their papers we have counts for
5 papers
Rapid yet accurate Tile-circuit and device modeling for Analog In-Memory Computing
J. Luquin, C. Mackin, S. Ambrogio +11
Analog In-Memory Compute (AIMC) can improve the energy efficiency of Deep Learning by orders of magnitude. Yet analog-domain device and circuit non-idealities -- within the analog…
Hardware-aware training for large-scale and diverse deep learning inference workloads using in-memory computing-based accelerators
Malte J. Rasch, Charles Mackin, Manuel Le Gallo +10
Analog in-memory computing (AIMC) -- a promising approach for energy-efficient acceleration of deep learning workloads -- computes matrix-vector multiplications (MVMs) but only app…
Overview of the IBM Neural Computer Architecture
Pritish Narayanan, Charles E. Cox, Alexis Asseman +4
The IBM Neural Computer (INC) is a highly flexible, re-configurable parallel processing system that is intended as a research and development platform for emerging machine intellig…
Deep Learning with Limited Numerical Precision
Suyog Gupta, Ankur Agrawal, Kailash Gopalakrishnan +1
Training of large-scale deep neural networks is often constrained by the available computational resources. We study the effect of limited precision data representation and computa…
Metal-Gated Junctionless Nanowire Transistors
Mostafizur Rahman, Pritish Narayanan, Csaba Andras Moritz
Junctionless Nanowire Field-Effect Transistors (JNFETs), where the channel region is uniformly doped without the need for source-channel and drain-channel junctions or lateral dopi…