In-Memory Computation of a Machine-Learning Classifier in a Standard 6T SRAM Array

By Jintao Zhang, Zhuo Wang, Naveen Verma. IEEE Journal of Solid-State Circuits, 2017

436 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/d1b53e3a977dffe016d88387e59e48d6dbdedcc6

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

1. Avishek Biswas
2. Anantha P. Chandrakasan

In Electrical and Electronic Engineering

1. Anantha P. Chandrakasan
2. Avishek Biswas
3. Naveen Verma
4. Massimo Alioto
5. Mohamed F. Younis
6. Xuxun Liu
7. Deborah Estrin
8. Ian F. Akyildiz

Citations in the corpus

• CONV-SRAM: An Energy-Efficient SRAM With In-Memory Dot-Product Computation for Low-Power Convolutional Neural Networks (2019, methodology): “signals have less variations typically, compared to those arising from local Vt mismatch in the pMOS devices [13].”
• In-Memory Computing: Advances and prospects (2019, methodology): “Consider accessing D bits of data from a D D # memory [7]: ■ Bandwidth: In the conventional architecture, bandwidth scales as D / 1 2 with the number of row-wise access cycles required.”