AivoTTA: An Energy Efficient Programmable Accelerator for CNN-Based Object Recognition

Abstract

Battery driven intelligent cameras used, e.g., in police operations or pico drone based surveillance require good object detection accuracy and low energy consumption at the same time. Object recognition algorithms based on Convolutional Neural Networks (CNN) currently produce the best accuracy, but require relatively high computational power. General purpose CPU and GPU implementations of CNN-based object recognition provide flexibility and performance, but this flexibility comes at a high energy cost. Fixed function hardware acceleration of CNNs provides the best energy efficiency, with a trade-off in reduced flexibility. This paper presents AivoTTA, a flexible and energy efficient CNN accelerator with a SIMD Transport-Triggered Architecture that is programmable in C and OpenCL C. The proposed accelerator makes use of smart memory access patterns and fusion of layers to greatly reduce the number of memory transfers and improve energy efficiency. The accelerator was synthesized using 28 nm ASIC technology for different supply voltages and clock frequencies. The most power efficient design points consume 11.3 mW for an object recognition network running 16 GOPS at 400 MHz. The maximum clock frequency is 1.4 GHz. With the maximum clock, the accelerator consumes 116 mW for an effective 57 GOPS. To the best of our knowledge, it is the most energy efficient compiler programmable CNN accelerator published.