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In-memory-computing (IMC) SRAM architecture has gained significant attention as it achieves high energy efficiency for computing a convolutional neural network (CNN) model [1]. Recent works investigated the use of analog-mixed-signal (AMS) hardware for high area and energy efficiency [2], [3]. However, AMS hardware output is well known to be susceptible to process, voltage, and temperature (PVT) variations, limiting the computing precision and ultimately the inference accuracy of a CNN. We reconfirmed, through the simulation of a capacitor-based IMC SRAM macro that computes a 256D binary dot product, that the AMS computing hardware has a significant root-mean-square error (RMSE) of 22.5% across the worst-case voltage, temperature (Fig. 16.1.1 top left) and 3-sigma process variations (Fig. 16.1.1 top right). On the other hand, we can implement an IMC SRAM macro using robust digital logic [4], which can virtually eliminate the variability issue (Fig. 16.1.1 top). However, digital circuits require more devices than AMS counterparts (e.g., 28 transistors for a mirror full adder [FA]). As a result, a recent digital IMC SRAM shows a lower area efficiency of 6368F2/b (22nm, 4b/4b weight/activation) [5] than the AMS counterpart (1170F2/b, 65nm, 1b/1b) [3]. In light of this, we aim to adopt approximate arithmetic hardware to improve area and power efficiency and present two digital IMC macros (DIMC) with different levels of approximation (Fig. 16.1.1 bottom left). Also, we propose an approximation-aware training algorithm and a number format to minimize inference accuracy degradation induced by approximate hardware (Fig. 16.1.1 bottom right). We prototyped a 28nm test chip: for a 1b/1b CNN model for CIFAR-10 and across 0.5-to-1.1V supply, the DIMC with double-approximate hardware (DIMC-D) achieves 2569F2/b, 932-2219TOPS/W, 475-20032GOPS, and 86.96% accuracy, while for a 4b/1b CNN model, the DIMC with the single-approximate hardware (DIMC-S) achieves 3814F2/b, 458-990TOPS/W
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TICA: A 0.3V, Variation-Resilient 64-Stage Deeply-Pipelined Bitcoin Mining Core with Timing Slack Inference and Clock Frequency Adaption
Energy-efficient bitcoin mining cores have gained significant attention since the energy cost for computing dominates the mining expenses [1]. Ultra-low-voltage (ULV) digital circuits have emerged as an attractive approach to improve the energy-efficiency. However, they demand a large timing margin for the worst-case process, voltage, and temperature (PVT) variations, undermining a significant portion of energy savings. Recent works, including multi-phase latch pipeline [1], tunable replica circuits [2]–[3], in-situ error detection and correction (EDAC) [4]–[6], and dynamic timing enhancement [7], can reduce the pessimistic margin. However, it is not straightforward to adopt those techniques in mining cores due to their deeply-pipelined architecture (up to 128 stages [1]). For example, to adopt EDAC, the deep pipeline requires inserting many bulky error detectors as it has many critical paths. Our experiment with a 0.3V 28-nm mining core shows >18.9% registers need to be replaced with error detectors, considering 6σ local process variation only. Also, multiple stages can have timing errors simultaneously, making an error correction process (e.g., clock gating [5], VDD boosting [6]) complex and costly.
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- Award ID(s):
- 1919147
- PAR ID:
- 10342206
- Date Published:
- Journal Name:
- 2022 IEEE Custom Integrated Circuits Conference (CICC)
- Page Range / eLocation ID:
- 01 to 02
- 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/10.1109/CICC53496.2022.9772803
