Abstract
Embedded Field-Programmable Gate Arrays (FPGAs) provide an efficient and flexible hardware platform to deploy highly optimised Deep Neural Network (DNN) accelerators. However, the limited area of embedded FPGAs restricts the degree of complexity of a DNN accelerator that can be deployed on them. Commonly an accelerator’s complexity is reduced to fit smaller FPGAs, often at the cost of significant redesign overhead. In this paper we present an alternative to this, which we call Temporal Accelerators. The main idea is to split an accelerator into smaller components, which are then executed by an FPGA sequentially. To do so, the FPGA is reconfigured multiple times during the execution of the accelerator. With this idea, we increase the available area of the FPGA ‘over time’. We show that modern FPGAs can reconfigure efficiently enough to achieve equally fast and energy efficient accelerators while using more cost efficient FPGAs. We develop and evaluate a Temporal Accelerator implementing an 1D Convolution Neural Network for detecting anomalies in ECG heart data. Our accelerator is deployed on a Xilinx Spartan 7 XC7S15. We compare it to a conventional implementation on the larger Xilinx Spartan 7 XC7S25. Our solution requires 9.06% less time to execute and uses 12.81% less energy while using an FPGA that is 35% cheaper.
The authors acknowledge the financial support by the Federal Ministry of Education and Research of Germany in the KI-Sprung LUTNet project (project number 16ES1125) as well as the KI-LiveS project (project number 895 01IS19068A).
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References
Burger, A., Qian, C., Schiele, G., Helms, D.: An embedded CNN implementation for on-device ECG analysis. In: 2020 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) (2020)
Han, S., Mao, H., Dally, W.J.: Deep compression: compressing deep neural networks with pruning, trained quantization and Huffman coding. arXiv preprint arXiv:1510.00149 (2015)
Iandola, F.N., Han, S., Moskewicz, M.W., Ashraf, K., Dally, W.J., Keutzer, K.: Squeezenet: Alexnet-level accuracy with 50\(\times \) fewer parameters and \(<\)0.5 MB model size. arXiv preprint arXiv:1602.07360 (2016)
McDanel, B., Teerapittayanon, S., Kung, H.T.: Embedded binarized neural networks. arXiv preprint arXiv:1709.02260 (2017)
Rastegari, M., Ordonez, V., Redmon, J., Farhadi, A.: XNOR-Net: ImageNet classification using binary convolutional neural networks. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9908, pp. 525–542. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46493-0_32
Wang, E., Davis, J.J., Cheung, P.Y., Constantinides, G.A.: Lutnet: rethinking inference in FPGA soft logic. In: 2019 IEEE 27th Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM). IEEE (2019)
Roth,W., et al.: Resource-efficient neural networks for embedded systems. arXiv preprint arXiv:2001.03048 (2020)
Hassibi, B., Stork, D.: Second order derivatives for network pruning: optimal brain surgeon. In: Advances in Neural Information Processing System (1993)
Yang, T.J., Chen, Y.H., Sze, V.: Designing energy-efficient convolutional neural networks using energy-aware pruning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2017)
Jouppi, N., Young, C., Patil, N., Patterson, D.: Motivation for and evaluation of the first tensor processing unit. IEEE Micro 38(3), 10–19 (2018)
Biswas, A., Chandrakasan, A.P.: CONV-SRAM: an energy-efficient SRAM with in-memory dot-product computation for low-power convolutional neural networks. IEEE J. Solid-State Circ. 54(1), 217–230 (2018)
Venieris, S.I., ouganis, C.S.: fpgaConvNet: a framework for mapping convolutional neural networks on FPGAs. In: 2016 IEEE 24th Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM) (2016)
Burger, A., Cichiwskyj, C., Schiele, G.: Elastic nodes for the Internet of Things: a middleware-based approach. In: Proceedings - 2017 IEEE International Conference on Autonomic Computing, ICAC 2017 (2017)
Cordone, R., Redaelli, F., Redaelli, M.A., Santambrogio, M.D., Sciuto, D.: Partitioning and scheduling of task graphs on partially dynamically reconfigurable FPGAs. IEEE Trans. Comput.-Aided Des. Integr. Circ. Syst. 28(5), 662–675 (2009)
Knocke, P., Gorgen, R., Walter, J., Helms, D., Nebel, W.: Using early power and timing estimations of massively heterogeneous computation platforms to create optimized HPC applications. In: Proceedings - 2014 International Conference on Embedded and Ubiquitous Computing, EUC 2014, 609757 (2014)
Schiele, G., Burger, A., Cichiwskyj, C.: The elastic node: an experimentation platform for hardware accelerator research in the Internet of Things. In: 2019 IEEE International Conference on Autonomic Computing (ICAC) (2019)
Burger, A., Schiele, G.: Demo abstract: deep learning on an elastic node for the Internet of Things. In: 2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) (2018)
Xilinx. 7 Series FPGAs Configuration User Guide - UG470 (2018)
Xilinx. 7 Series FPGAs Data Sheet: Overview (2018)
Xilinx. Spartan-7 FPGAs Data Sheet: DC and AC Switching Characteristics (2019)
Xilinx. Xilinx Power Estimator (XPE) (2020)
Xilinx. Power Methodology Guide - UG786 (v14.5) (2018)
Inc. Micron Technology. Micron Flash Memory Support for Xilinx Platforms (2019)
Mouser. Electronic Components Distributor - Mouser Electronics Germany (2020)
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Cichiwskyj, C., Qian, C., Schiele, G. (2020). Time to Learn: Temporal Accelerators as an Embedded Deep Neural Network Platform. In: Gama, J., et al. IoT Streams for Data-Driven Predictive Maintenance and IoT, Edge, and Mobile for Embedded Machine Learning. ITEM IoT Streams 2020 2020. Communications in Computer and Information Science, vol 1325. Springer, Cham. https://doi.org/10.1007/978-3-030-66770-2_19
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