A thermal-aware optimization framework for ReRAM-based deep neural network acceleration

Hyein Shin, Myeonggu Kang, Lee‐Sup Kim

202023 citationsDOI

Abstract

Resistive RAM (ReRAM) is widely regarded as a promising platform for deep neural network (DNN) acceleration. However, the ReRAM device suffers from severe thermal problems that degrade the lifetime and inference accuracy of the ReRAM-based DNN accelerator. To address the issues, we propose a thermal-aware optimization framework for accelerating DNN on ReRAM (TOPAR). TOPAR includes 3-stage offline thermal optimization and online thermal-aware error compensation. Offline thermal optimization consists of thermal-aware weight decomposition, thermal-aware column reordering, and fine-grained weight adjustment to reduce the temperature of the ReRAM-based DNN accelerator. For online thermal-aware error compensation, we compensate conductance change according to the temperature variation. With TOPAR, the endurance degradation due to temperature rise improves up to 2.39×, and inference accuracy is preserved without harming the performance of the ReRAM-based DNN accelerator.

Topics & Concepts

Resistive random-access memoryComputer scienceAccelerationCompensation (psychology)Artificial neural networkInferenceThermalArtificial intelligenceElectrical engineeringVoltageEngineeringPhysicsPsychologyMeteorologyClassical mechanicsPsychoanalysisAdvanced Memory and Neural ComputingFerroelectric and Negative Capacitance DevicesMachine Learning in Materials Science