Low Cost Convolutional Neural Network Accelerator Based on Bi-Directional Filtering and Bit-Width Reduction

Woong Choi, Kyungrak Choi, Jongsun Park

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)


This paper presents a low-area and energy-efficient hardware accelerator for the convolutional neural networks (CNNs). Based on the multiply-accumulate-based architecture, three design techniques are proposed to reduce the hardware cost of the convolutional computations. First, to reduce the computational cost of convolutions, an adaptive bit-width reduction combined with near-zero skipping is proposed based on differential input method (DIM). The DIM-based design technique can reduce 62.5% of operation bit-width and improve 17.0% of activation sparsity with almost ignorable CNN accuracy degradation. Second, it has been found that adopting a bi-directional filtering window in a CNN accelerator can considerably reduce the energy for data movement with a much smaller number of memory accesses. To expedite the bi-directional filtering operations, we also propose a bi-directional first-input-first-output (bi-FIFO). With SRAM bit-cell layout manner, the proposed bi-FIFO facilitates fast data re-distribution with area and energy efficiency. To verify the effectiveness of the proposed techniques, the AlexNet accelerator has been designed. The numerical results show that the proposed adaptive bit-width reduction scheme achieves 34.6% and 58.2% of area and energy savings, respectively. The bi-FIFO-based accelerator also achieves 32.8% improved processing time.

Original languageEnglish
Pages (from-to)14734-14746
Number of pages13
JournalIEEE Access
Publication statusPublished - 2018 Mar 14

Bibliographical note

Funding Information:
This work was supported in part by the National Research Foundation of Korea grant through the Korean Government under Grant NRF-2016R1A2B4015329 and Grant NRF-2015M3D1A1070465 and in part by the Industrial Strategic Technology Development Program (Development of SoC technology based on Spiking Neural Cell for smart mobile and IoT Devices) through the Ministry of Trade, Industry & Energy, South Korea, under Grant 10077445.

Publisher Copyright:
© 2013 IEEE.


  • Deep neural network
  • FIFO
  • accelerator
  • activation sparsity
  • convolutional neural network
  • energy efficiency
  • line buffer
  • quantization

ASJC Scopus subject areas

  • General Computer Science
  • General Materials Science
  • General Engineering


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