Performance Evaluation of GraphCore IPU-M2000 Accelerator for Text Detection Application

Abstract

The large compute load and memory footprint of modern deep neural networks motivates the use of accelerators for high through- put deployments in application spanning multiple domains. In this paper, we evaluate throughput capabilities of a comparatively new hardware from Graphcore, IPU-M2000 that supports massive par- allelism and in-memory compute. For a text detection model, we measured the throughput and power variations with batch size. We also evaluate compressed versions of this model and analyze perfor- mance variation with model precision. Additionally, we compare IPU (Intelligence Processing Unit) results with state-of-the-art GPU and FPGA deployments of a compute intensive text region detec- tion application. Our experiments suggest, IPU supports superior throughput, 27×, 1.89×, and 1.56× as compared to CPU, FPGA DPU and A100 GPU, respectively for text detection application.