Weightless Neural Networks for Efficient Edge Inference

• URL: http://arxiv.org/abs/2203.01479v1
• Date: Thu, 3 Mar 2022 01:46:05 GMT
• Title: Weightless Neural Networks for Efficient Edge Inference
• Authors: Zachary Susskind, Aman Arora, Igor Dantas Dos Santos Miranda, Luis Armando Quintanilla Villon, Rafael Fontella Katopodis, Leandro Santiago de Araujo, Diego Leonel Cadette Dutra, Priscila Machado Vieira Lima, Felipe Maia Galvao Franca, Mauricio Breternitz Jr., and Lizy K. John
• Abstract summary: Weightless Neural Networks (WNNs) are a class of machine learning model which use table lookups to perform inference. We propose a novel WNN architecture, BTHOWeN, with key algorithmic and architectural improvements over prior work. BTHOWeN targets the large and growing edge computing sector by providing superior latency and energy efficiency.
• Score: 1.7882696915798877
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Weightless Neural Networks (WNNs) are a class of machine learning model which use table lookups to perform inference. This is in contrast with Deep Neural Networks (DNNs), which use multiply-accumulate operations. State-of-the-art WNN architectures have a fraction of the implementation cost of DNNs, but still lag behind them on accuracy for common image recognition tasks. Additionally, many existing WNN architectures suffer from high memory requirements. In this paper, we propose a novel WNN architecture, BTHOWeN, with key algorithmic and architectural improvements over prior work, namely counting Bloom filters, hardware-friendly hashing, and Gaussian-based nonlinear thermometer encodings to improve model accuracy and reduce area and energy consumption. BTHOWeN targets the large and growing edge computing sector by providing superior latency and energy efficiency to comparable quantized DNNs. Compared to state-of-the-art WNNs across nine classification datasets, BTHOWeN on average reduces error by more than than 40% and model size by more than 50%. We then demonstrate the viability of the BTHOWeN architecture by presenting an FPGA-based accelerator, and compare its latency and resource usage against similarly accurate quantized DNN accelerators, including Multi-Layer Perceptron (MLP) and convolutional models. The proposed BTHOWeN models consume almost 80% less energy than the MLP models, with nearly 85% reduction in latency. In our quest for efficient ML on the edge, WNNs are clearly deserving of additional attention.

Related papers

• GhostRNN: Reducing State Redundancy in RNN with Cheap Operations [66.14054138609355]
  We propose an efficient RNN architecture, GhostRNN, which reduces hidden state redundancy with cheap operations. Experiments on KWS and SE tasks demonstrate that the proposed GhostRNN significantly reduces the memory usage (40%) and computation cost while keeping performance similar.
  arXiv  Detail & Related papers  (2024-11-20T11:37:14Z)
• NAS-BNN: Neural Architecture Search for Binary Neural Networks [55.058512316210056]
  We propose a novel neural architecture search scheme for binary neural networks, named NAS-BNN. Our discovered binary model family outperforms previous BNNs for a wide range of operations (OPs) from 20M to 200M. In addition, we validate the transferability of these searched BNNs on the object detection task, and our binary detectors with the searched BNNs achieve a novel state-of-the-art result, e.g., 31.6% mAP with 370M OPs, on MS dataset.
  arXiv  Detail & Related papers  (2024-08-28T02:17:58Z)
• Hardware-Aware DNN Compression via Diverse Pruning and Mixed-Precision Quantization [1.0235078178220354]
  We propose an automated framework to compress Deep Neural Networks (DNNs) in a hardware-aware manner by jointly employing pruning and quantization. Our framework achieves $39%$ average energy reduction for datasets $1.7%$ average accuracy loss and outperforms significantly the state-of-the-art approaches.
  arXiv  Detail & Related papers  (2023-12-23T18:50:13Z)
• Can Deep Neural Networks be Converted to Ultra Low-Latency Spiking Neural Networks? [3.2108350580418166]
  Spiking neural networks (SNNs) operate via binary spikes distributed over time. SOTA training strategies for SNNs involve conversion from a non-spiking deep neural network (DNN) We propose a new training algorithm that accurately captures these distributions, minimizing the error between the DNN and converted SNN.
  arXiv  Detail & Related papers  (2021-12-22T18:47:45Z)
• Elastic-Link for Binarized Neural Network [9.83865304744923]
  "Elastic-Link" (EL) module enrich information flow within a BNN by adaptively adding real-valued input features to the subsequent convolutional output features. EL produces a significant improvement on the challenging large-scale ImageNet dataset. With the integration of ReActNet, it yields a new state-of-the-art result of 71.9% top-1 accuracy.
  arXiv  Detail & Related papers  (2021-12-19T13:49:29Z)
• DTNN: Energy-efficient Inference with Dendrite Tree Inspired Neural Networks for Edge Vision Applications [2.1800759000607024]
  We propose Dendrite-Tree based Neural Network (DTNN) for energy-efficient inference with table lookup operations enabled by activation quantization. DTNN achieved significant energy saving (19.4X and 64.9X improvement on ResNet-18 and VGG-11 with ImageNet, respectively) with negligible loss of accuracy.
  arXiv  Detail & Related papers  (2021-05-25T11:44:12Z)
• ANNETTE: Accurate Neural Network Execution Time Estimation with Stacked Models [56.21470608621633]
  We propose a time estimation framework to decouple the architectural search from the target hardware. The proposed methodology extracts a set of models from micro- kernel and multi-layer benchmarks and generates a stacked model for mapping and network execution time estimation. We compare estimation accuracy and fidelity of the generated mixed models, statistical models with the roofline model, and a refined roofline model for evaluation.
  arXiv  Detail & Related papers  (2021-05-07T11:39:05Z)
• FracBNN: Accurate and FPGA-Efficient Binary Neural Networks with Fractional Activations [20.218382369944152]
  Binary neural networks (BNNs) have 1-bit weights and activations. BNNs tend to produce a much lower accuracy on realistic datasets such as ImageNet. This work proposes FracBNN, which exploits fractional activations to substantially improve the accuracy of BNNs.
  arXiv  Detail & Related papers  (2020-12-22T17:49:30Z)
• FATNN: Fast and Accurate Ternary Neural Networks [89.07796377047619]
  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. In this work, we show that, under some mild constraints, computational complexity of the ternary inner product can be reduced by a factor of 2. We elaborately design an implementation-dependent ternary quantization algorithm to mitigate the performance gap.
  arXiv  Detail & Related papers  (2020-08-12T04:26:18Z)
• 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)
• PatDNN: Achieving Real-Time DNN Execution on Mobile Devices with Pattern-based Weight Pruning [57.20262984116752]
  We introduce a new dimension, fine-grained pruning patterns inside the coarse-grained structures, revealing a previously unknown point in design space. With the higher accuracy enabled by fine-grained pruning patterns, the unique insight is to use the compiler to re-gain and guarantee high hardware efficiency.
  arXiv  Detail & Related papers  (2020-01-01T04:52:07Z)

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.