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TIMAQ: A Time-Domain Computing-in-Memory-Based Processor Using Predictable Decomposed Convolution for Arbitrary Quantized DNNs
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DOI：10.1109/JSSC.2021.3095232.png)
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Energy-efficient processors are crucial for accelerating deep neural networks (DNNs) on edge devices with limited battery capacity. To reduce energy consumption, time-domain computing-in-memory (TD-CIM) is a splendid architecture, which consumes low computation and memory access energy due to low toggle rate of time-based signals and less data movements, respectively. When deploying DNNs in TD-CIMs, quantization is required, which has two types: uniform quantization (UQ) and nonuniform quantization (NUQ). To reach the same accuracy for one DNN, NUQ achieves smaller model size than UQ. Due to varying weight distributions across layers, mixed-precision quantization can further reduce model size, without degrading accuracy. However, previous TD-CIMs are inefficient for mixed-precision NUQ-DNNs due to their adopted bit-serial convolution increasing computation amount significantly. To address that, we propose a unique-weight convolution to accelerate mixed-precision NUQ-DNNs by a special kernel decomposition, reducing computation count remarkably. Based on that, we design a TD-CIM-based processor, TIMAQ, with three architectural techniques: 1) bit-cross-flipping-based kernel decomposer to reduce memory accesses and operations of decomposing kernels; 2) dual-mode-complementary predictor to remove redundant computations; and 3) activation-weight-adaptive pulse quantizer to decrease pulse quantization energy and error. Fabricated in 28-nm CMOS technology and tested on 1-8-b NUQ-DNNs, TIMAQ achieves 2.4-152.7-TOPS/W peak energy efficiency.
Keyword:
Indexes
Kernel
Convolution
Quantization (signal)
Time-domain analysis
Memory management
Delays
Computing-in-memory (CIM)
deep neural network (DNN) processor
nonuniform quantization (NUQ)
redundant computation
time-domain (TD) computing
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5.6
论文数:
923
被引数:
2.7W
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