Computing In Spintronic Memory: A Thermal Perspective

TL;DR

This paper provides a quantitative thermal analysis of computing-in-memory (CiM), revealing how temperature distribution depends on activity, array size, and technology, crucial for thermal management.

Contribution

It offers the first detailed thermal characterization of CiM, highlighting the impact of activity, array size, and technology on temperature behavior.

Findings

Temperature remains mostly uniform due to lateral thermal conduction.

Temperature increases linearly with the number of active memory cells.

Temperature decreases linearly with larger memory array size.

Abstract

Computing-in-Memory (CiM) is a promising paradigm to address the memory bottleneck constraining traditional systems. Most power-efficient CiM variants can directly perform Boolean operations in non-volatile memory arrays. Higher microarchitectural activity due to CiM, however, can significantly increase power density (power per area) and result in thermal hotspots. In this paper, we provide a quantitative thermal characterization for CiM. We demonstrate that (i) the temperature remains mostly uniform due to lateral thermal conduction; (ii) the temperature increases linearly with the number of memory cells participating in computation; (iii) the temperature decreases linearly with the memory array size; (iv) the memory technology dictates the power density, hence the thermal characteristics.