Abstract
Large-scale deep convolutional neural networks (CNNs) are widely used in machine learning applications. While CNNs involve huge complexity, VLSI (ASIC and FPGA) chips that deliver high-density integration of computational resources are regarded as a promising platform for CNN's implementation. At massive parallelism of computational units, however, the external memory bandwidth, which is constrained by the pin count of the VLSI chip, becomes the system bottleneck. Moreover, VLSI solutions are usually regarded as a lack of the flexibility to be reconfigured for the various parameters of CNNs. This paper presents CNN-MERP to address these issues. CNN-MERP incorporates an efficient memory hierarchy that significantly reduces the bandwidth requirements from multiple optimizations including on/off-chip data allocation, data flow optimization and data reuse. The proposed 2-level reconfigurability is utilized to enable fast and efficient reconfiguration, which is based on the control logic and the multiboot feature of FPGA. As a result, an external memory bandwidth requirement of 1.94MB/GFlop is achieved, which is 55% lower than prior arts. Under limited DRAM bandwidth, a system throughput of 1244GFlop/s is achieved at the Vertex UltraScale platform, which is 5.48 times higher than the state-of-the-art FPGA implementations.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 34th IEEE International Conference on Computer Design, ICCD 2016 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 320-327 |
| Number of pages | 8 |
| ISBN (Electronic) | 9781509051427 |
| DOIs | |
| Publication status | Published - 2016 Nov 22 |
| Event | 34th IEEE International Conference on Computer Design, ICCD 2016 - Scottsdale, United States Duration: 2016 Oct 2 → 2016 Oct 5 |
Other
| Other | 34th IEEE International Conference on Computer Design, ICCD 2016 |
|---|---|
| Country | United States |
| City | Scottsdale |
| Period | 16/10/2 → 16/10/5 |
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Keywords
- backward propagation
- convolutional neural networks
- FPGA
- memory bandwidth
- reconfigurable processor
ASJC Scopus subject areas
- Hardware and Architecture
Cite this
CNN-MERP : An FPGA-based memory-efficient reconfigurable processor for forward and backward propagation of convolutional neural networks. / Han, Xushen; Zhou, Dajiang; Wang, Shihao; Kimura, Shinji.
Proceedings of the 34th IEEE International Conference on Computer Design, ICCD 2016. Institute of Electrical and Electronics Engineers Inc., 2016. p. 320-327 7753296.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - CNN-MERP
T2 - An FPGA-based memory-efficient reconfigurable processor for forward and backward propagation of convolutional neural networks
AU - Han, Xushen
AU - Zhou, Dajiang
AU - Wang, Shihao
AU - Kimura, Shinji
PY - 2016/11/22
Y1 - 2016/11/22
N2 - Large-scale deep convolutional neural networks (CNNs) are widely used in machine learning applications. While CNNs involve huge complexity, VLSI (ASIC and FPGA) chips that deliver high-density integration of computational resources are regarded as a promising platform for CNN's implementation. At massive parallelism of computational units, however, the external memory bandwidth, which is constrained by the pin count of the VLSI chip, becomes the system bottleneck. Moreover, VLSI solutions are usually regarded as a lack of the flexibility to be reconfigured for the various parameters of CNNs. This paper presents CNN-MERP to address these issues. CNN-MERP incorporates an efficient memory hierarchy that significantly reduces the bandwidth requirements from multiple optimizations including on/off-chip data allocation, data flow optimization and data reuse. The proposed 2-level reconfigurability is utilized to enable fast and efficient reconfiguration, which is based on the control logic and the multiboot feature of FPGA. As a result, an external memory bandwidth requirement of 1.94MB/GFlop is achieved, which is 55% lower than prior arts. Under limited DRAM bandwidth, a system throughput of 1244GFlop/s is achieved at the Vertex UltraScale platform, which is 5.48 times higher than the state-of-the-art FPGA implementations.
AB - Large-scale deep convolutional neural networks (CNNs) are widely used in machine learning applications. While CNNs involve huge complexity, VLSI (ASIC and FPGA) chips that deliver high-density integration of computational resources are regarded as a promising platform for CNN's implementation. At massive parallelism of computational units, however, the external memory bandwidth, which is constrained by the pin count of the VLSI chip, becomes the system bottleneck. Moreover, VLSI solutions are usually regarded as a lack of the flexibility to be reconfigured for the various parameters of CNNs. This paper presents CNN-MERP to address these issues. CNN-MERP incorporates an efficient memory hierarchy that significantly reduces the bandwidth requirements from multiple optimizations including on/off-chip data allocation, data flow optimization and data reuse. The proposed 2-level reconfigurability is utilized to enable fast and efficient reconfiguration, which is based on the control logic and the multiboot feature of FPGA. As a result, an external memory bandwidth requirement of 1.94MB/GFlop is achieved, which is 55% lower than prior arts. Under limited DRAM bandwidth, a system throughput of 1244GFlop/s is achieved at the Vertex UltraScale platform, which is 5.48 times higher than the state-of-the-art FPGA implementations.
KW - backward propagation
KW - convolutional neural networks
KW - FPGA
KW - memory bandwidth
KW - reconfigurable processor
UR - http://www.scopus.com/inward/record.url?scp=85006705647&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85006705647&partnerID=8YFLogxK
U2 - 10.1109/ICCD.2016.7753296
DO - 10.1109/ICCD.2016.7753296
M3 - Conference contribution
SP - 320
EP - 327
BT - Proceedings of the 34th IEEE International Conference on Computer Design, ICCD 2016
PB - Institute of Electrical and Electronics Engineers Inc.
ER -