Training large scale convolutional neural networks (CNNs) is an extremely computation and memory intensive task that requires massive computational resources and training time. Recently, many accelerator solutions have been proposed to improve the performance and efficiency of CNNs. Existing approaches mainly focus on the inference phase of CNN, and can hardly address the new challenges posed in CNN training: the resource requirement diversity and bidirectional data dependency between convolutional layers (CVLs) and fully-connected layers (FCLs). To overcome this problem, this paper presents a new accelerator architecture for CNN training, called TNPU, which leverages the complementary effect of the resource requirements between CVLs and FCLs. Unlike prior approaches optimizing CVLs and FCLs in separate way, we take an alternative by smartly orchestrating the computation of CVLs and FCLs in single computing unit to work concurrently so that both computing and memory resources will maintain high utilization, thereby boosting the performance. We also proposed a simplified out-of-order scheduling mechanism to address the bidirectional data dependency issues in CNN training. The experiments show that TNPU achieves a speedup of 1.5x and 1.3x, with an average energy reduction of 35.7% and 24.1% over comparably provisioned state-of-the-art accelerators (DNPU and DaDianNao), respectively.

Convolutional Neural Networks (CNNs) have been widely used in machine learning tasks. While delivering state-of-the-art accuracy, CNNs are known as both compute- and memory-intensive. This paper presents the SqueezeFlow accelerator architecture that exploits sparsity of CNN models for increased efficiency. Unlike prior accelerators that trade complexity for flexibility, SqueezeFlow exploits concise convolution rules to benefit from the reduction of computation and memory accesses as well as the acceleration of existing dense architectures without intrusive PE modifications. Specifically, SqueezeFlow employs a PT-OS-sparse dataflow that removes the ineffective computations while maintaining the regularity of CNN computations. We present a full design down to the layout at 65 nm, with an area of 4.80 mm2and power of 536.09 mW. The experiments show that SqueezeFlow achieves a speedup of 2:9× on VGG16 compared to the dense architectures, with an area and power overhead of only 8.8 and 15.3 percent, respectively. On three representative sparse CNNs, SqueezeFlow improves the performance and energy efficiency by 1:8× and 1:5× over the state-of-the-art sparse accelerators.

Advances in computing contribute to science and engineering discovery, innovation, and education by facilitating representations, processing, storage, analysis, simulation, and visualization of unprecedented amounts of experimental and observational data to address problems that affect health, energy, environment, security, and quality of life. In spite of the emerging importance of the role of computing in engineering, a well-recognized shortage of scientists and engineers who are adequately prepared to take advantage of, or contribute to, such highly interdisciplinary, highly computational scientific challenges is evident. This exploratory study identifies how computation is integrated in the engineering disciplines at the undergraduate level. The research question is: How engineering professors integrate computation as part of their disciplinary undergraduate courses? This study reports anonymous survey responses of thirty-nine engineering and engineering technology faculty members who identified themselves as integrating computation as part of their undergraduate courses. Results indicate that most of the faculty members used computation for the solution of complex calculations, for conducting simulations, and for design purposes. Further research is required in order to identify and validate appropriated pedagogical practices to integrate computation as part of disciplinary courses.