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Latest algorithmic development has brought competitive classification accuracy for neural networks despite constraining the network parameters to ternary or binary representations. These findings show significant optimization opportunities to replace computationally-intensive convolution operations (based on multiplication) with more efficient and less complex operations such as addition. In hardware implementation domain, processing-in-memory architecture is becoming a promising solution to alleviate enormous energy-hungry data communication between memory and processing units, bringing considerable improvement for system performance and energy efficiency while running such large networks. In this paper, we review several of our recent works regarding Processing-in-Memory (PIM) accelerator based on Magnetic Random Access Memory computational sub-arrays to accelerate the inference mode of quantized neural networks using digital non-volatile memory rather than using analog crossbar operation. In this way, we investigate the performance of two distinct in-memory addition schemes compared to other digital methods based on processing-in-DRAM/GPU/ASIC design to tackle DNN power and memory wall bottleneck.
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Local Binary Pattern Networks
Emerging edge devices such as sensor nodes are increasingly being tasked with non-trivial tasks related to sensor data processing and even application-level inferences from this sensor data. These devices are, however, extraordinarily resource-constrained in terms of CPU power (often Cortex M0-3 class CPUs), available memory (in few KB to MBytes), and energy. Under these constraints, we explore a novel approach to character recognition using local binary pattern networks, or LBPNet, that can learn and perform bit-wise operations in an end-to-end fashion. LBPNet has its advantage for characters whose features are composed of structured strokes and distinctive outlines. LBPNet uses local binary comparisons and random projections in place of conventional convolution (or approximation of convolution) operations, providing an important means to improve memory efficiency as well as inference speed. We evaluate LBPNet on a number of character recognition benchmark datasets as well as several object classification datasets and demonstrate its effectiveness and efficiency.
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- PAR ID:
- 10166838
- Date Published:
- Journal Name:
- IEEE Winter Conference on Applications of Computer Vision
- ISSN:
- 2472-6796
- Page Range / eLocation ID:
- 825-834
- 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/WACV45572.2020.9093550
