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QuantMAC: Enhancing Hardware Performance in DNNs With Quantize Enabled Multiply-Accumulate Unit
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DOI：10.1109/ACCESS.2024.3379906.png)
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Abstract
En 中文
In response to the escalating demand for hardware-efficient Deep Neural Network (DNN) architectures, we present a novel quantize-enabled multiply-accumulate (MAC) unit. Our methodology employs a right shift-and-add computation for MAC operation, enabling runtime truncation without additional hardware. This architecture optimally utilizes hardware resources, enhancing throughput performance while reducing computational complexity through bit-truncation techniques. Our key methodology involves designing a hardware-efficient MAC computational algorithm that supports both iterative and pipeline implementations, catering to diverse hardware efficiency or enhanced throughput requirements in accelerators. Additionally, we introduce a processing element (PE) with a pre-loading bias scheme, reducing one clock delay and eliminating the need for conventional extra resources in PE implementation. The PE facilitates quantization-based MAC calculations through an efficient bit-truncation method, removing the necessity for extra hardware logic. This versatile PE accommodates variable bit-precision with a dynamic fraction part within the sfxpt< N,f > representation, meeting specific model or layer demands. Through software emulation, our proposed approach demonstrates minimal accuracy loss, revealing under 1.6% loss for LeNet-5 using MNIST and around 4% for ResNet-18 and VGG-16 with CIFAR-10 in the sfxpt< 8 ,5 > format compared to conventional float32-based implementations. Hardware performance parameters on the Xilinx-Virtex-7 board unveil a 37% reduction in area utilization and a 45% reduction in power consumption compared to the best state-of-the-art MAC architecture. Extending the proposed MAC to a LeNet DNN model results in a 42% reduction in resource requirements and a significant 27% reduction in delay. This architecture provides notable advantages for resource-efficient, high-throughput edge-AI applications.
Keywords:
Computer architecture
Hardware
Artificial neural networks
Throughput
Artificial intelligence
Arithmetic
Convolution
Approximate compute
bit-truncation
CORDIC
deep neural network
hardware accelerator
quantize processing element
Journal
IF:
3.6
Papers:
9.8W
Citations:
29.4W