312 citations · 397 across the 14 of their papers we have counts for
4 papers · 1 filter
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…
Generalized Key-Value Memory to Flexibly Adjust Redundancy in Memory-Augmented Networks
Denis Kleyko, Geethan Karunaratne, Jan M. Rabaey +2
Memory-augmented neural networks enhance a neural network with an external key-value memory whose complexity is typically dominated by the number of support vectors in the key memo…
ESSOP: Efficient and Scalable Stochastic Outer Product Architecture for Deep Learning
Vinay Joshi, Geethan Karunaratne, Manuel Le Gallo +5
Deep neural networks (DNNs) have surpassed human-level accuracy in a variety of cognitive tasks but at the cost of significant memory/time requirements in DNN training. This limits…
5 Parallel Prism: A topology for pipelined implementations of convolutional neural networks using computational memory
Martino Dazzi, Abu Sebastian, Pier Andrea Francese +3
In-memory computing is an emerging computing paradigm that could enable deeplearning inference at significantly higher energy efficiency and reduced latency. The essential idea is…