SPARCNet

Deep neural networks have been shown to outperform prior state-of-the-art solutions that often relied heavily on hand-engineered feature extraction techniques coupled with simple classification algorithms. In particular, deep convolutional neural networks have been shown to dominate on several popular public benchmarks such as the ImageNet database. Unfortunately, the benefits of deep networks have yet to be fully exploited in embedded, resource-bound settings that have strict power and area budgets. Graphical processing unit (GPU) have been shown to improve throughput and energy-efficiency over central processing unit (CPU) due to their highly parallel architecture yet still impose a significant power burden. In a similar fashion, field programmable gate array (FPGA) can be used to improve performance while further allowing more fine-grained control over implementation to improve efficiency. In order to reduce power and area while still achieving required throughput, classification-efficient network architectures are required in addition to optimal deployment on efficient hardware. In this work, we target both of these enterprises. For the first objective, we analyze simple, biologically inspired reduction strategies that are applied both before and after training. The central theme of the techniques is the introduction of sparsification to help dissolve away the dense connectivity that is often found at different levels in convolutional neural networks. The sparsification techniques include feature compression partition , structured filter pruning , and dynamic feature pruning . Additionally, we explore filter factorization and filter quantization approximation techniques to further reduce the complexity of convolutional layers. In the second contribution, we propose SPARCNet, a hardware accelerator for efficient deployment of SPAR se C onvolutional NET works. The accelerator looks to enable deploying networks in such resource-bound settings by both exploiting efficient forms of parallelism inherent in convolutional layers and by exploiting the sparsification and approximation techniques proposed. To demonstrate both contributions, modern deep convolutional network architectures containing millions of parameters are explored within the context of the computer vision dataset CIFAR. Utilizing the reduction techniques, we demonstrate the ability to reduce computation and memory by 60% and 93% with less than 0.03% impact on accuracy when compared to the best baseline network with 93.47% accuracy. The SPARCNet accelerator with different numbers of processing engines is implemented on a low-power Artix-7 FPGA platform. Additionally, the same networks are optimally implemented on a number of embedded commercial-off-the-shelf platforms including NVIDIAs CPU+GPU SoCs TK1 and TX1 and Intel Edison. Compared to NVIDIAs TK1 and TX1, the FPGA-based accelerator obtains 11.8 × and 7.5 × improvement in energy efficiency while maintaining a classification throughput of 72 images/s. When further compared to a number of recent FPGA-based accelerators, SPARCNet is able to achieve up to 15 × improvement in energy efficiency while consuming less than 2W of total board power at 100MHz. In addition to improving efficiency, the accelerator has built-in support for sparsification techniques and ability to perform in-place rectified linear unit (ReLU) activation function, max-pooling, and batch normalization.