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Stochastic Computing for Deep Neural Networks
| Content Provider | Scilit |
|---|---|
| Author | Bodiwala, Sunny Nanavati, Nirali |
| Copyright Year | 2021 |
| Description | Deep neural networks (DNNs) have been revolutionizing machine learning research and various applications by achieving better accuracy than human perception. However, a large amount of computational resources are needed due to their complex topologies, restricting the usage of DNNs in embedded systems and portable devices with limited areas and power budgets. This chapter considers stochastic computing (SC) as an alternative to binarized computing. SC uses a random bit sequence where the signal value is computed by counting the number of ones in the bit sequence. This allows the implementation of major arithmetic units like multiplication and addition by a simple logic. Consequently, SC can implement DNNs with essentially low hardware overhead and also offers good scalability. First, the chapter presents a detailed study of processing elements used in neural networks. Then, combining several building blocks, such as stochastic approximation of activation functions in SC, saturation arithmetic, and scaling, a new scheme is proposed for DNN inference and training in SC. At last, the chapter shows that SC-compatible DNNs can be effectively deployed on low-cost hardware. Deep neural network, optimization, accelerator, custom computing, stochastic computing Book Name: Computing Technologies and Applications |
| Related Links | https://api.taylorfrancis.com/content/chapters/edit/download?identifierName=doi&identifierValue=10.1201/9781003166702-9&type=chapterpdf |
| Ending Page | 178 |
| Page Count | 18 |
| Starting Page | 161 |
| DOI | 10.1201/9781003166702-9 |
| Language | English |
| Publisher | Informa UK Limited |
| Publisher Date | 2021-09-20 |
| Access Restriction | Open |
| Subject Keyword | Book Name: Computing Technologies and Applications Deep Neural Network Computing Building Optimization Functions |
| Content Type | Text |
| Resource Type | Chapter |
