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Communication Dans Un Congrès Année : 2021

A 43pJ per Inference CBNN-based Compute-in-sensor Associative Memory in 28nm FDSOI

Résumé

Distributed smart sensors are more and more used in applications such as biomedical or domestic monitoring. However, each sensor broadcasts data wirelessly to the others or to an aggregator, which leads to energy-hungry sensor nodes and an increased latency at the network level. To tackle both challenges, this work proposes to distribute part of the processing elements in each sensor node and presents a 28nm FDSOI ASIC implementation of an associative memory using clique-based neural networks (CBNNs) coupled with an integrated SRAM memory. It consumes 43pJ for a single inference, which is 6.5 times better than state-of-the-art associative memories implementations, for the same memory size.
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Dates et versions

hal-03482304 , version 1 (17-12-2021)

Identifiants

Citer

Benoit Larras, Antoine Frappé. A 43pJ per Inference CBNN-based Compute-in-sensor Associative Memory in 28nm FDSOI. ESSCIRC 2021 - IEEE 47th European Solid State Circuits Conference (ESSCIRC), Sep 2021, Grenoble, France. pp.111-114, ⟨10.1109/ESSCIRC53450.2021.9567808⟩. ⟨hal-03482304⟩
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