Year: 2020
Type: dissertation
Abstract: High-dimensional matrix-vector-multiplications (MVM) are the main operations of deep neural networks (DNN). As the size of DNNs increases, data movement becomes a problem and limits their performance. Analog in-memory computing accelerators are one of the most promising solutions to reduce this problem. This project designs an in-memory computing solution that employs charge-domain compute using 22FDX technology. The design, called multiplying bit cell (M-BC), consists of an 8T bit cell and a MOM capacitor. The design is part of an architecture of 8x8 = 64 neuron tiles that performs the filtering operation of up to 3x3x512 input activation (IA). Each neuron tile is composed of 64x64 = 4096 neuron patches. The design achieves energy efficiency of 1170 TOPS/W and throughput of 18876 GOPS.
Author: Unai Echeverria Olaiz
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3155310884