Arbitrary Precision Printed Ternary Neural Networks with Holistic Evolutionary Approximation
Printed electronics offer a promising alternative for applications beyond silicon-based systems, requiring properties like flexibility, stretchability, conformality, and ultra-low fabrication costs. Despite the large feature sizes in printed electronics, printed neural networks have attracted attention for meeting target application requirements, though realizing complex circuits remains challenging. This work bridges the gap between classification accuracy and area efficiency in printed neural networks, covering the entire processing-near-sensor system design and co-optimization from the analog-to-digital interface-a major area and power bottleneck-to the digital classifier. We propose an automated framework for designing printed Ternary Neural Networks with arbitrary input precision, utilizing multi-objective optimization and holistic approximation. Our circuits outperform existing approximate printed neural networks by 17x in area and 59x in power on average, being the first to enable printed-battery-powered operation with under 5% accuracy loss while accounting for analog-to-digital interfacing costs.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Trained Ternary Quantization
Deep neural networks are widely used in machine learning applications. However, the deployment of large neural networks models can be difficult to deploy on mobile devices with limited power budgets. To solve this proble…
QuantizationHyperspherical Loss-Aware Ternary Quantization
Most of the existing works use projection functions for ternary quantization in discrete space. Scaling factors and thresholds are used in some cases to improve the model accuracy. However, the gradients used for optimiz…
image-classificationImage Classificationobject-detectionObject Detection+1Compressing deep neural networks on FPGAs to binary and ternary precision with HLS4ML
We present the implementation of binary and ternary neural networks in the hls4ml library, designed to automatically convert deep neural network models to digital circuits with FPGA firmware. Starting from benchmark mode…
Handwritten Digit RecognitionTiM-DNN: Ternary in-Memory accelerator for Deep Neural Networks
The use of lower precision has emerged as a popular technique to optimize the compute and storage requirements of complex Deep Neural Networks (DNNs). In the quest for lower precision, recent studies have shown that tern…
GPUImage ClassificationLanguage ModellingFATNN: Fast and Accurate Ternary Neural Networks
Ternary Neural Networks (TNNs) have received much attention due to being potentially orders of magnitude faster in inference, as well as more power efficient, than full-precision counterparts. However, 2 bits are require…
image-classificationImage ClassificationQuantization