Data Reuse for Accelerated Approximate Warps

• Published in: IEEE transactions on computer-aided design of integrated circuits and systems Vol. 39; no. 12; pp. 4623 - 4634
• Main Authors: Peroni, Daniel, Imani, Mohsen, Nejatollahi, Hamid, Dutt, Nikil, Rosing, Tajana
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.12.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Acceleration; Accuracy; Approximate computing; Approximation; Associative memory; Biological neural networks; Computation; Computer vision; Energy consumption; energy efficiency; floating-point unit (FPU); GPU; Graphics processing units; Instruction sets; Lookup tables; Machine learning; Mathematical analysis; Neural networks; Neurons; Speech recognition; Warp; warps
• ISSN: 0278-0070; 1937-4151
• DOI: 10.1109/TCAD.2020.2986128

Many data-driven applications, including computer vision, machine learning, speech recognition, and medical diagnostics show tolerance to computation error. These applications are often accelerated on GPUs, but the performance improvements require high energy usage. In this article, we present DRAAW, an approximate computing technique capable of accelerating GPGPU applications at a warp level. In GPUs, warps are groups of threads which issued together across multiple cores. The slowest thread dictates the pace of the warp, so DRAAW identifies these bottlenecks and avoids them during approximation. We alleviate computation costs by using an approximate lookup table which tracks recent operations and reuses them to exploit temporal locality within applications. To improve neural network performance, we propose neuron aware approximation, a technique which profiles operations within network layers and automatically configures DRAAW to ensure computations with more impact on the output accuracy are subject to less approximation. We evaluate our design by placing DRAAW within each core of an Nvidia Kepler Architecture Titan. DRAAW improves throughput by up to <inline-formula> <tex-math notation="LaTeX">2.8\times </tex-math></inline-formula> and improves energy-delay product (EDP) by <inline-formula> <tex-math notation="LaTeX">5.6\times </tex-math></inline-formula> for six GPGPU applications while maintaining less than 5% output error. We show neuron aware approximation accelerates the inference of six neutral networks by <inline-formula> <tex-math notation="LaTeX">2.9\times </tex-math></inline-formula> and improves EDP by <inline-formula> <tex-math notation="LaTeX">6.2\times </tex-math></inline-formula> with less than 1% impact on prediction accuracy.