This work presents AIDA, an associative in-memory deep learning processor for edge devices. An associative processor is a massively parallel non-von Neumann accelerator that uses memory cells for computing; the bulk of data is never transferred outside the memory arrays for external processing. AIDA utilizes a dynamic content addressable memory for both data storage and processing, and benefits from sparsity and limited arithmetic precision, typical in modern deep neural networks (DNNs). The novel in-data processing implementation designed for the AIDA accelerator achieves a speedup of 270× over an advanced CPU at more than three orders-of-magnitude better energy efficiency.

AIDA: Associative In-memory Deep learning Accelerator

Garzon Esteban
;
Lanuzza M.;
2022-01-01

Abstract

This work presents AIDA, an associative in-memory deep learning processor for edge devices. An associative processor is a massively parallel non-von Neumann accelerator that uses memory cells for computing; the bulk of data is never transferred outside the memory arrays for external processing. AIDA utilizes a dynamic content addressable memory for both data storage and processing, and benefits from sparsity and limited arithmetic precision, typical in modern deep neural networks (DNNs). The novel in-data processing implementation designed for the AIDA accelerator achieves a speedup of 270× over an advanced CPU at more than three orders-of-magnitude better energy efficiency.
Adders
Arithmetic
Arrays
Deep learning
Logic gates
Random access memory
Registers
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11770/338055
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