English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

In this paper, we propose a reconfigurable architecture that supports various convolutional neural networks (CNNs) such as GoogLeNet and AlexNet. The proposed architecture mainly includes 24 parallel PEs (processing engines) for image data convolution processing, each engine containing 9x4 MAC (multiplier-accumulator) units. Through the combination of PE, this structure can support a variety of bit-width convolution operations, namely 8bit×8bit, 16bit×8bit, 16bit×16bit. At the same time, it also supports a variety of sizes of convolution operation, that is, 1×1, 3×3, 5×5, 7×7. The architecture is synthesized using 65-nm TSMC technology and achieves a peak of 1105.9 GOPS at 640MHz, 1V, and a power consumption of 193mW. Compared with the existing AlexNet architecture, the proposed architecture improves the computational efficiency by 20% to 27.4%.

Design of the key Structure of Convolutional Neural Network Reconfigurable Accelerator Based on ASIC