In-memory computing

++++ 2017 Efficient Processing of Deep Neural Networks: A Tutorial and Survey Vivienne Sze

• 2017 Hardware for Machine Learning: Challenges and Opportunities (Invited Paper) Vivienne Sze

• Data movement - In order to meet computing demands in terms of power and speed, need to redesign computing hardware from the ground up Vivienne Sze - technical talk

• A Microprocessor implemented in 65nm CMOS with Configurable and Bit-scalable Accelerator for Programmable In-memory Computing Naveen Verma

https://www.intrinsicsemi.com/

Memristors--from In-memory computing, Deep Learning Acceleration, Spiking Neural Networks, to the Future of Neuromorphic and Bio-inspired Computing
A Mehonic, A Sebastian, B Rajendran, O Simeone, E Vasilaki, AJ Kenyon

UCL spun-out Intrinsic closes £1.35M seed funding to develop next-gen memory devices

+++ 2020 In‐Memory Vector‐Matrix Multiplication in Monolithic Complementary Metal–Oxide–Semiconductor‐Memristor Integrated Circuits: Design Choices, Challenges, and Perspectives

ReRam based .... frequently analog-based

flexibility versus energy efficiency
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2020 In‐Memory Binary Vector–Matrix Multiplication Based on Complementary Resistive Switches