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With the success of deep neural networks (DNN), many recent works have been focusing on developing hardware accelerator for power and resource-limited embedded system via model compression techniques, such as quantization, pruning, low-rank approximation, etc. However, almost all existing DNN structure is fixed after deployment, which lacks runtime adaptive DNN structure to adapt to its dynamic hardware resource, power budget, throughput requirement, as well as dynamic workload. Correspondingly, there is no runtime adaptive hardware platform to support dynamic DNN structure. To address this problem, we first propose a dynamic channel-adaptive deep neural network (CA-DNN) which can adjust the involved convolution channel (i.e. model size, computing load) at run-time (i.e. at inference stage without retraining) to dynamically trade off between power, speed, computing load and accuracy. Further, we utilize knowledge distillation method to optimize the model and quantize the model to 8-bits and 16-bits, respectively, for hardware friendly mapping. We test the proposed model on CIFAR-10 and ImageNet dataset by using ResNet. Comparing with the same model size of individual model, our CA-DNN achieves better accuracy. Moreover, as far as we know, we are the first to propose a Processing-in-Memory accelerator for such adaptive neural networks structure based on Spin Orbit Torque Magnetic Random Access Memory(SOT-MRAM) computational adaptive sub-arrays. Then, we comprehensively analyze the trade-off of the model with different channel-width between the accuracy and the hardware parameters, eg., energy, memory, and area overhead.
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OPA: One-Predict-All For Efficient Deployment
Deep neural network (DNN) is the de facto standard for running a variety of computer vision applications over mobile and embedded systems. Prior to deployment, a DNN is specialized by training to fit the target use scenario (depending on computing power and visual data input). To handle its costly training and meet diverse deployment needs, a “Train Once, Deploy Everywhere” paradigm has been recently proposed by training one super-network and selecting one out of many sub-networks (part of the super-network) for the target scenario; This empowers efficient DNN deployment at low training cost (training once). However, the existing studies tackle some deployment factors like computing power and source data but largely overlook the impact of their runtime dynamics (say, time-varying visual contents and GPU/CPU workloads). In this work, we propose OPA to cover all these deployment factors, particularly those along with runtime dynamics in visual data contents and computing resources. To quickly and accurately learn which sub-network runs “best” in the dynamic deployment scenario, we devise a “One-Predict-All” approach with no need to run all the candidate sub-networks. Instead, we first develop a shallow sub-network to test the water and then use its test results to predict the performance of all other deeper sub-networks. We have implemented and evaluated OPA. Compared to the state-of-the-art, OPA has achieved up to 26% higher Top-1 accuracy for a given latency requirement.
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- Award ID(s):
- 1750953
- PAR ID:
- 10419997
- Date Published:
- Journal Name:
- INFOCOM
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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