Building accurate and efficient deep neural network (DNN) models for intelligent sensing systems to process data locally is essential. Spiking neural networks (SNNs) have gained significant popularity in recent years because they are more biological-plausible and energy-efficient than DNNs. However, SNNs usually have lower accuracy than DNNs. In this paper, we propose to use SNNs for image sensing applications. Moreover, we introduce the DNN-SNN knowledge distillation algorithm to reduce the accuracy gap between DNNs and SNNs. Our DNNSNN knowledge distillation improves the accuracy of an SNN by transferring knowledge between a DNN and an SNN. To better transfer the knowledge, our algorithm creates two learning paths from a DNN to an SNN. One path is between the output layer and another path is between the intermediate layer. DNNs use real numbers to propagate information between neurons while SNNs use 1-bit spikes. To empower the communication between DNNs and SNNs, we utilize a decoder to decode spikes into real numbers. Also, our algorithm creates a learning path from an SNN to a DNN. This learning path better adapts the DNN to the SNN by allowing the DNN to learn the knowledge from the SNN. Our SNN models are deployed on Loihi, which is a specialized chip for SNN models. On the MNIST dataset, our SNN models trained by the DNN-SNN knowledge distillation achieve better accuracy than the SNN models on GPU trained by other training algorithms with much lower energy consumption per image.
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Transfer Learning for Performance Modeling of Deep Neural Network Systems
Modern deep neural network (DNN) systems are highly configurable with large a number of options that significantly affect their non-functional behavior, for example inference time and energy consumption. Performance models allow to understand and predict the effects of such configuration options on system behavior, but are costly to build because of large configuration spaces. Performance models from one environment cannot be transferred directly to another; usually models are rebuilt from scratch for different environments, for example different hardware. Recently, transfer learning methods have been applied to reuse knowledge from performance models trained in one environment in another. In this paper, we perform an empirical study to understand the effectiveness of different transfer learning strategies for building performance models of DNN systems. Our results show that transferring information on the most influential configuration options and their interactions is an effective way of reducing the cost to build performance models in new environments.
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- Award ID(s):
- 1813537
- NSF-PAR ID:
- 10096516
- Date Published:
- Journal Name:
- USENIX Conference on Operational Machine Learning
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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- The DOI is not currently available.
