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Ultra-fast & low-power superconductor single-flux-quantum (SFQ)-based CNN systolic accelerators are built to enhance the CNN inference throughput. However, shift-register (SHIFT)-based scratchpad memory (SPM) arrays prevent a SFQ CNN accelerator from exceeding 40% of its peak throughput, due to the lack of random access capability. This paper first documents our study of a variety of cryogenic memory technologies, including Vortex Transition Memory (VTM), Josephson-CMOS SRAM, MRAM, and Superconducting Nanowire Memory, during which we found that none of the aforementioned technologies made a SFQ CNN accelerator achieve high throughput, small area, and low power simultaneously. Second, we present a heterogeneous SPM architecture, SMART, composed of SHIFT arrays and a random access array to improve the inference throughput of a SFQ CNN systolic accelerator. Third, we propose a fast, low-power and dense pipelined random access CMOS-SFQ array by building SFQ passive-transmission-line-based H-Trees that connect CMOS sub-banks. Finally, we create an ILP-based compiler to deploy CNN models on SMART. Experimental results show that, with the same chip area overhead, compared to the latest SHIFT-based SFQ CNN accelerator, SMART improves the inference throughput by 3.9 × (2.2 ×), and reduces the inference energy by 86% (71%) when inferring a single image (a batch of images).
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LightBulb: A Photonic-Nonvolatile-Memory-based Accelerator for Binarized Convolutional Neural Networks
Although Convolutional Neural Networks (CNNs) have demonstrated the state-of-the-art inference accuracy in various intelligent applications, each CNN inference involves millions of expensive floating point multiply-accumulate (MAC) operations. To energy-efficiently process CNN inferences, prior work proposes an electro-optical accelerator to process power-of-2 quantized CNNs by electro-optical ripple-carry adders and optical binary shifters. The electro-optical accelerator also uses SRAM registers to store intermediate data. However, electro-optical ripple-carry adders and SRAMs seriously limit the operating frequency and inference throughput of the electro-optical accelerator, due to the long critical path of the adder and the long access latency of SRAMs. In this paper, we propose a photonic nonvolatile memory (NVM)-based accelerator, Light-Bulb, to process binarized CNNs by high frequency photonic XNOR gates and popcount units. LightBulb also adopts photonic racetrack memory to serve as input/output registers to achieve high operating frequency. Compared to prior electro-optical accelerators, on average, LightBulb improves the CNN inference throughput by 17× ~ 173× and the inference throughput per Watt by 17.5 × ~ 660×.
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- Award ID(s):
- 1908992
- PAR ID:
- 10167914
- Date Published:
- Journal Name:
- Design, Automation & Test in Europe Conference & Exhibition
- Page Range / eLocation ID:
- 1438 to 1443
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.23919/DATE48585.2020.9116494
