AoCStream: All-on-Chip CNN Accelerator with Stream-Based Line-Buffer Architecture and Accelerator-Aware Pruning

• Published in: Sensors (Basel, Switzerland) Vol. 23; no. 19; p. 8104
• Main Authors: Kang, Hyeong-Ju, Yang, Byung-Do
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 27.09.2023; MDPI
• Subjects: Algorithms; CNN accelerator; convolutional neural networks; Neural networks; pruning; Technology application
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s23198104

Convolutional neural networks (CNNs) play a crucial role in many EdgeAI and TinyML applications, but their implementation usually requires external memory, which degrades the feasibility of such resource-hungry environments. To solve this problem, this paper proposes memory-reduction methods at the algorithm and architecture level, implementing a reasonable-performance CNN with the on-chip memory of a practical device. At the algorithm level, accelerator-aware pruning is adopted to reduce the weight memory amount. For activation memory reduction, a stream-based line-buffer architecture is proposed. In the proposed architecture, each layer is implemented by a dedicated block, and the layer blocks operate in a pipelined way. Each block has a line buffer to store a few rows of input data instead of a frame buffer to store the whole feature map, reducing intermediate data-storage size. The experimental results show that the object-detection CNNs of MobileNetV1/V2 and an SSDLite variant, widely used in TinyML applications, can be implemented even on a low-end FPGA without external memory.