Special Session: ADAPT: ANN-ControlleD System-Level Runtime Adaptable APproximate CompuTing

Abstract

Approximate computing has shown to be an effective approach to generate smaller and more power-efficient circuits by trading the accuracy of the circuit vs. area and/or power. So far, most work on approximate computing has focused on specific components within a system. It severely limits the approximation potential as most Integrated Circuits (ICs) are now complex heterogeneous systems. One additional limitation of current work in this domain is they assume that the training data matches the actual workload. This is nevertheless not always true as these complex Systems-on-Chip (SoCs) are used for a variety of different applications. To address these issues, this work investigates if lower-power designs can be found through mixing approximations across the different components in the SoC as opposed to only aggressively approximating a single component. The main hypothesis is that some approximations amplify across the system, while others tend to cancel each other out, thus, allowing to maximize the power savings while meeting the given maximum error threshold. To investigate this, we propose a method called ADAPT. ADAPT uses a neural network-based controller to dynamically adjust the supply voltage (Vdd) of different components in SoC at runtime based on the actual workload.