Approximate inference systems (AxIS): end-to-end approximations for energy-efficient inference at the edge

Abstract

The rapid proliferation of the Internet-of-Things (IoT) and the dramatic resurgence of artificial intelligence (AI) based application workloads has led to immense interest in performing inference on energy-constrained edge devices. Approximate computing (a design paradigm that yields large energy savings at the cost of a small degradation in application quality) is a promising technique to enable energy-efficient inference at the edge. This paper introduces the concept of an approximate inference system (AxIS) and proposes a systematic methodology to perform joint approximations across different subsystems in a deep neural network-based inference system, leading to significant energy benefits compared to approximating individual subsystems in isolation. We use a smart camera system that executes various convolutional neural network (CNN) based image recognition applications to illustrate how the sensor, memory, compute, and communication subsystems can all be approximated synergistically. We demonstrate our proposed methodology using two variants of a smart camera system: (a) Camedge, where the CNN executes locally on the edge device, and (b) Camcloud, where the edge device sends the captured image to a remote cloud server that executes the CNN. We have prototyped such an approximate inference system using an Altera Stratix IV GX-based Terasic TR4-230 FPGA development board. Experimental results obtained using six CNNs demonstrate significant energy savings (around 1.7× for Camedge and 3.5× for Camcloud) for minimal (< 1%) loss in application quality. Compared to approximating a single subsystem in isolation, AxIS achieves additional energy benefits of 1.6×--1.7× (Camedge) and 1.4×--3.4× (Camcloud) on average for minimal application-level quality loss.