Journal of the Korea Institute of Information and Communication Engineering (한국정보통신학회논문지)

The Korea Institute of Information and Commucation Engineering (한국정보통신학회)
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Microcode based Controller for Compact CNN Accelerators Aimed at Mobile Devices

모바일 디바이스를 위한 소형 CNN 가속기의 마이크로코드 기반 컨트롤러

  • Na, Yong-Seok (Department of Electronics Engineering, Chungbuk National University) ;
  • Son, Hyun-Wook (Department of Electronics Engineering, Chungbuk National University) ;
  • Kim, Hyung-Won (Department of Electronics Engineering, Chungbuk National University)
  • Received : 2021.12.22
  • Accepted : 2022.01.15
  • Published : 2022.03.31
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Abstract

This paper proposes a microcode-based neural network accelerator controller for artificial intelligence accelerators that can be reconstructed using a programmable architecture and provide the advantages of low-power and ultra-small chip size. In order for the target accelerator to support various neural network models, the neural network model can be converted into microcode through microcode compiler and mounted on accelerator to control the operators of the accelerator such as datapath and memory access. While the proposed controller and accelerator can run various CNN models, in this paper, we tested them using the YOLOv2-Tiny CNN model. Using a system clock of 200 MHz, the Controller and accelerator achieved an inference time of 137.9 ms/image for VOC 2012 dataset to detect object, 99.5ms/image for mask detection dataset to detect wearing mask. When implementing an accelerator equipped with the proposed controller as a silicon chip, the gate count is 618,388, which corresponds to 65.5% reduction in chip area compared with an accelerator employing a CPU-based controller (RISC-V).

본 논문은 프로그램 가능한 구조를 사용하여 재구성이 가능하고 저 전력 초소형의 장점을 모두 제공하는 인공지능 가속기를 위한 마이크로코드 기반 뉴럴 네트워크 가속기 컨트롤러를 제안한다. 대상 가속기가 다양한 뉴럴 네트워크 모델을 지원하도록 마이크로코드 컴파일러를 통해 뉴럴 네트워크 모델을 마이크로코드로 변환하여 가속기의 메모리 접근과 모든 연산기를 제어할 수 있다. 200MHz의 System Clock을 기준으로 설계하였으며, YOLOv2-Tiny CNN model을 구동하도록 컨트롤러를 구현하였다. 객체 감지를 위한 VOC 2012 dataset 추론용 컨트롤러를 구현할 경우 137.9ms/image, mask 착용 여부 감지를 위한 mask detection dataset 추론용으로 구현할 경우 99.5ms/image의 detection speed를 달성하였다. 제안된 컨트롤러를 탑재한 가속기를 실리콘칩으로 구현할 때 게이트 카운트는 618,388이며, 이는 CPU core로서 RISC-V (U5-MC2)를 탑재할 경우 대비 약 65.5% 감소한 칩 면적을 제공한다.

Keywords

Acknowledgement

This work was partly supported by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government(MSIT) (No.2020-0-01304, Development of Self-learnable Mobile Recursive Neural Network Processor Technology) and also supported by the MSIT(Ministry of Science and ICT), Korea, under the Grand Information Technology Research Center support program(IITP-2020-0-01462) and supervised by the IITP(Institute for Information & communications Technology Planning & Evaluation) and also supported by the AURI(Korea Association of University, Research institute and Industry) grant funded by the Korea Government(MSS : Ministry of SMEs and Startups). (No. S2929950, HRD program for 2020) and supported by National R&D Program through the National Research Foundation of Korea(NRF) funded by Ministry of Science and ICT(No. 2020M3H2A1076786).

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