898 citations · 1.5k across the 22 of their papers we have counts for
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End-to-End DNN Inference on a Massively Parallel Analog In Memory Computing Architecture
Nazareno Bruschi, Giuseppe Tagliavini, Angelo Garofalo +4
The demand for computation resources and energy efficiency of Convolutional Neural Networks (CNN) applications requires a new paradigm to overcome the "Memory Wall". Analog In-Memo…
Benchmarking energy consumption and latency for neuromorphic computing in condensed matter and particle physics
Dominique J. Kösters, Bryan A. Kortman, Irem Boybat +10
The massive use of artificial neural networks (ANNs), increasingly popular in many areas of scientific computing, rapidly increases the energy consumption of modern high-performanc…
Scale up your In-Memory Accelerator: Leveraging Wireless-on-Chip Communication for AIMC-based CNN Inference
Nazareno Bruschi, Giuseppe Tagliavini, Francesco Conti +7
Analog In-Memory Computing (AIMC) is emerging as a disruptive paradigm for heterogeneous computing, potentially delivering orders of magnitude better peak performance and efficienc…
ALPINE: Analog In-Memory Acceleration with Tight Processor Integration for Deep Learning
Joshua Klein, Irem Boybat, Yasir Qureshi +6
Analog in-memory computing (AIMC) cores offers significant performance and energy benefits for neural network inference with respect to digital logic (e.g., CPUs). AIMCs accelerate…
A Heterogeneous In-Memory Computing Cluster For Flexible End-to-End Inference of Real-World Deep Neural Networks
Angelo Garofalo, Gianmarco Ottavi, Francesco Conti +4
Deployment of modern TinyML tasks on small battery-constrained IoT devices requires high computational energy efficiency. Analog In-Memory Computing (IMC) using non-volatile memory…