![]() In addition, system optimization techniques are presented to improve the throughput further. In the proposed hardware architecture, a neural processing unit (NPU) that consists of heterogeneous units, such as band processing, scaling, and accumulating, and data fetching and formatting units is designed to accelerate the DNNs efficiently. In this article, we propose an efficient computing system for real-time SSDLite object detection on FPGA devices, which includes novel hardware architecture and system optimization techniques. Although several field-programmable gate array (FPGA) implementations have been presented recently for real-time object detection, they suffer from either low throughput or low detection accuracy. However, it is hard to employ the DNNs in embedded systems due to their high computational complexity and deep-layered structure. As a result, GANPU enables on-device training of GANs with high energy efficiency.ĭeep neural network (DNN)-based object detection has been investigated and applied to various real-time applications. Fabricated in a 65-nm process, the GANPU achieved the energy efficiency of 75.68 TFLOPS/W for 16-bit floating-point computation, which is 4.85 Moreover, an exponent-only ReLU speculation (EORS) algorithm is proposed along with its lightweight processing element (PE) architecture, to estimate the location of output feature zeros during the inference with minimal hardware overhead. To take advantage of ReLU sparsity during both inference and training, dual-sparsity exploitation architecture is proposed to skip redundant computations due to input and output feature zeros. An adaptive spatiotemporal workload multiplexing is proposed to maintain high utilization in accelerating multiple DNNs in a single GAN model. ![]() For higher throughput and energy efficiency, this article proposed three key features. Besides, networks and layers in GANs show dramatically changing operational characteristics, making it difficult to optimize the processor’s core and bandwidth allocation. Training GANs require a massive amount of computation, and therefore, it is difficult to accelerate in a resource-constrained platform. It enables on-device training of GANs on performance- and battery-limited mobile devices, without sending user-specific data to servers, fully evading privacy concerns. This article presents generative adversarial network processing unit (GANPU), an energy-efficient multiple deep neural network (DNN) training processor for GANs.
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