EncodingNet: A Novel Encoding-based MAC Design for Efficient Neural Network Acceleration

• URL: http://arxiv.org/abs/2402.18595v2
• Date: Wed, 06 Nov 2024 07:28:12 GMT
• Title: EncodingNet: A Novel Encoding-based MAC Design for Efficient Neural Network Acceleration
• Authors: Bo Liu, Grace Li Zhang, Xunzhao Yin, Ulf Schlichtmann, Bing Li,
• Abstract summary: We propose a novel digital multiply-accumulate (MAC) design based on encoding. In this new design, the multipliers are replaced by simple logic gates to represent the results with a wide bit representation. Since the multiplication function is replaced by a simple logic representation, the critical paths in the resulting circuits become much shorter.
• Score: 7.694043781601237
• License:
• Abstract: Deep neural networks (DNNs) have achieved great breakthroughs in many fields such as image classification and natural language processing. However, the execution of DNNs needs to conduct massive numbers of multiply-accumulate (MAC) operations on hardware and thus incurs a large power consumption. To address this challenge, we propose a novel digital MAC design based on encoding. In this new design, the multipliers are replaced by simple logic gates to represent the results with a wide bit representation. The outputs of the new multipliers are added by bit-wise weighted accumulation and the accumulation results are compatible with existing computing platforms accelerating neural networks. Since the multiplication function is replaced by a simple logic representation, the critical paths in the resulting circuits become much shorter. Correspondingly, pipelining stages and intermediate registers used to store partial sums in the MAC array can be reduced, leading to a significantly smaller area as well as better power efficiency. The proposed design has been synthesized and verified by ResNet18- Cifar10, ResNet20-Cifar100, ResNet50-ImageNet, MobileNetV2-Cifar10, MobileNetV2-Cifar100, and EfficientNetB0-ImageNet. The experimental results confirmed the reduction of circuit area by up to 48.79% and the reduction of power consumption of executing DNNs by up to 64.41%, while the accuracy of the neural networks can still be well maintained. The open source code of this work can be found on GitHub with link https://github.com/Bo-Liu-TUM/EncodingNet/.

Related papers

• Quality Scalable Quantization Methodology for Deep Learning on Edge [0.20718016474717196]
  Deep Learning Architectures employ heavy computations and bulk of the computational energy is taken up by the convolution operations in the Convolutional Neural Networks. The proposed work is to reduce the energy consumption and size of CNN for using machine learning techniques in edge computing on ubiquitous computing devices. The experiments done on LeNet and ConvNets show an increase upto 6% of zeros and memory savings upto 82.4919% while keeping the accuracy near the state of the art.
  arXiv  Detail & Related papers  (2024-07-15T22:00:29Z)
• BasisN: Reprogramming-Free RRAM-Based In-Memory-Computing by Basis Combination for Deep Neural Networks [9.170451418330696]
  We propose BasisN framework to accelerate deep neural networks (DNNs) on any number of crossbars without reprogramming. We show that cycles per inference and energy-delay product were reduced to below 1% compared with applying reprogramming on crossbars.
  arXiv  Detail & Related papers  (2024-07-04T08:47:05Z)
• A&B BNN: Add&Bit-Operation-Only Hardware-Friendly Binary Neural Network [5.144744286453014]
  A&B BNN is proposed to remove part of the multiplication operations in a traditional BNN and replace the rest with an equal number of bit operations. The mask layer can be removed during inference by leveraging the intrinsic characteristics of BNN. The quantized RPReLU structure enables more efficient bit operations by constraining its slope to be integer powers of 2.
  arXiv  Detail & Related papers  (2024-03-06T14:28:49Z)
• Saving RNN Computations with a Neuron-Level Fuzzy Memoization Scheme [0.0]
  Recurrent Neural Networks (RNNs) are a key technology for applications such as automatic speech recognition or machine translation. We build a neuron-level fuzzy memoization scheme, which dynamically caches each neuron's output and reuses it whenever it is predicted that the current output will be similar to a previously computed result. We show that our technique avoids more than 26.7% of computations, resulting in 21% energy savings and 1.4x speedup on average.
  arXiv  Detail & Related papers  (2022-02-14T09:02:03Z)
• Quantized Neural Networks via {-1, +1} Encoding Decomposition and Acceleration [83.84684675841167]
  We propose a novel encoding scheme using -1, +1 to decompose quantized neural networks (QNNs) into multi-branch binary networks. We validate the effectiveness of our method on large-scale image classification, object detection, and semantic segmentation tasks.
  arXiv  Detail & Related papers  (2021-06-18T03:11:15Z)
• Adder Neural Networks [75.54239599016535]
  We present adder networks (AdderNets) to trade massive multiplications in deep neural networks. In AdderNets, we take the $ell_p$-norm distance between filters and input feature as the output response. We show that the proposed AdderNets can achieve 75.7% Top-1 accuracy 92.3% Top-5 accuracy using ResNet-50 on the ImageNet dataset.
  arXiv  Detail & Related papers  (2021-05-29T04:02:51Z)
• ShiftAddNet: A Hardware-Inspired Deep Network [87.18216601210763]
  ShiftAddNet is an energy-efficient multiplication-less deep neural network. It leads to both energy-efficient inference and training, without compromising expressive capacity. ShiftAddNet aggressively reduces over 80% hardware-quantified energy cost of DNNs training and inference, while offering comparable or better accuracies.
  arXiv  Detail & Related papers  (2020-10-24T05:09:14Z)
• Efficient Integer-Arithmetic-Only Convolutional Neural Networks [87.01739569518513]
  We replace conventional ReLU with Bounded ReLU and find that the decline is due to activation quantization. Our integer networks achieve equivalent performance as the corresponding FPN networks, but have only 1/4 memory cost and run 2x faster on modern GPU.
  arXiv  Detail & Related papers  (2020-06-21T08:23:03Z)
• Widening and Squeezing: Towards Accurate and Efficient QNNs [125.172220129257]
  Quantization neural networks (QNNs) are very attractive to the industry because their extremely cheap calculation and storage overhead, but their performance is still worse than that of networks with full-precision parameters. Most of existing methods aim to enhance performance of QNNs especially binary neural networks by exploiting more effective training techniques. We address this problem by projecting features in original full-precision networks to high-dimensional quantization features.
  arXiv  Detail & Related papers  (2020-02-03T04:11:13Z)
• AdderNet: Do We Really Need Multiplications in Deep Learning? [159.174891462064]
  We present adder networks (AdderNets) to trade massive multiplications in deep neural networks for much cheaper additions to reduce computation costs. We develop a special back-propagation approach for AdderNets by investigating the full-precision gradient. As a result, the proposed AdderNets can achieve 74.9% Top-1 accuracy 91.7% Top-5 accuracy using ResNet-50 on the ImageNet dataset.
  arXiv  Detail & Related papers  (2019-12-31T06:56:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.