Accelerating SVM on Ultra Low Power ASIP for High Throughput Streaming Applications

Abstract

With increasing complexity of algorithms for embedded systems, demand for higher processor performance and lower battery power consumption is growing immensely. Due to upcoming fields like embedded vision where algorithms require learning, techniques like Support Vector Machines (SVM) have gained significant importance in these areas. These machines are required in performing classification tasks in variety of fields to analyze data, recognize patterns in images and videos. In this work, SVM is implemented on an Application Specific Instruction Processor (ASIP) designed using an Architectural Description Language (ADL) based tool to meet the ultra-high throughput and ultra-low power requirement posed by pedestrian detection algorithm in embedded vision-domain. We started with a base RISC processor and added a list of systematic extensions to gain speed for SVM like algorithms. With this we could achieve a throughput of ~630K SVMs/sec (~3k dimensions) at 6.5 mW, which is significantly better than GPU (Nvidia GTX280 at 236 Watt) in terms of power and ARM Cortex-A8 (~16K SVMs/sec) in terms of throughput.