인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
On-device learning enables edge devices to continuously adapt to new data for AI applications. Leveraging sparsity to eliminate redundant computation and storage usage during training is a key approach to improving the learning efficiency of edge deep neural network(DNN). However, due to the lack of assumptions about non-zero positions, expensive runtime identification and allocation of zero positions and load balancing of irregular computations are often required, making it difficult for existing sparse training works to approach the ideal speedup. This paper points out that if the non-zero position constraints of operands during training can be predicted in advance, these processing overheads can be skipped to improve sparse training energy efficiency. Therefore, this paper explores the position constraint rules between operands for three typical activation functions in edge scenarios during sparse training. And based on these rules, this paper proposed a parev hardware-friendly sparse training algorithm to reduce the computation and storage pressure of the three phases, and an energy-efficient sparse training accelerator that can be executed in parallel with the forward propagation computation to estimate the non-zero positions so that the runtime processing cost is masked. Experiments show that the proposed method is 2.2 times, 1.38 times and 1.46 times more energy efficient than dense accelerator and two other sparse training tasks respectively.
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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