Tiny ci-SAR A/D Converter for Deep Neural Networks in Analog in-Memory Computation-Reference-Cited by-同舟云学术

Tiny ci-SAR A/D Converter for Deep Neural Networks in Analog in-Memory Computation

Published:2022-05-28 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE International Symposium on Circuits and Systems (ISCAS)
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Author:

Caselli Michele¹,Bhattacharjee Debjyoti²,Mallik Arindam²,Debacker Peter²,Verkest Diederik²

Affiliation:

1. KU Leuven

2. imec Leuven

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9937201/9937203/09937613.pdf?arnumber=9937613

Reference11 articles.

1. Charge Sharing and Charge Injection A/D Converters for Analog In-Memory Computing

2. Quantization Driven ADC Range Control for Analog in Memory Deep Neural Networks Acceleration;laubeuf;accepted for ACM Transactions on Design Automation of Electronic Systems,2021

3. CONV-SRAM: An Energy-Efficient SRAM With In-Memory Dot-Product Computation for Low-Power Convolutional Neural Networks

4. A 22 nm, 1540 TOP/s/W, 12.1 TOP/s/mm2 in-Memory Analog Matrix-Vector-Multiplier for DNN Acceleration

5. Design-Technology Space Exploration for Energy Efficient AiMC-Based Inference Acceleration

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