Convolutional Neural Networks (CNNs) have shown a great deal of success in diverse application domains including computer vision, speech recognition, and natural language processing. However, as the size of datasets and the depth of neural network architectures continue to grow, it is imperative to design high-performance and energy-efficient computing hardware for training CNNs. In this paper, we consider the problem of designing specialized CPU-GPU based heterogeneous manycore systems for energy-efficient training of CNNs. It has already been shown that the typical on-chip communication infrastructures employed in conventional CPU-GPU based heterogeneous manycore platforms are unable to handle both CPU and GPU communication requirements efficiently. To address this issue, we first analyze the on-chip traffic patterns that arise from the computational processes associated with training two deep CNN architectures, namely, LeNet and CDBNet, to perform image classification. By leveraging this knowledge, we design a hybrid Network-on-Chip (NoC) architecture, which consists of both wireline and wireless links, to improve the performance of CPU-GPU based heterogeneous manycore platforms running the above-mentioned CNN training workloads. The proposed NoC achieves 1.8× reduction in network latency and improves the network throughput by a factor of 2.2 for training CNNs, when compared to a highly-optimized wireline mesh NoC. For the considered CNN workloads, these network-level improvements translate into 25% savings in full-system energy-delay-product (EDP). This demonstrates that the proposed hybrid NoC for heterogeneous manycore architectures is capable of significantly accelerating training of CNNs while remaining energy-efficient.
In recent years, designing specialized manycore heterogeneous architectures for deep learning kernels has become an area of great interest. However, the typical on-chip communication infrastructures employed on conventional manycore platforms are unable to handle both CPU and GPU communication requirements efficiently. Hence, in this paper, our aim is to enhance the performance of heterogeneous manycore architectures through the design of a hybrid NoC consisting of both wireline and wireless links. To this end, we specifically target the resource-intensive backpropagation algorithm commonly used as the training method in deep learning. For backpropagation, the proposed hybrid NoC achieves 1.9X reduction in network latency and improves the network throughput by a factor of 2 with respect to a highly optimized mesh NoC. These network level improvements translate into 25% savings in full system energy-delay-product (EDP). This demonstrates the capability of the proposed hybrid and heterogeneous manycore architecture in accelerating deep learning kernels in an energy-efficient manner.
Wireless NoC is an emerging paradigm to design highbandwidth and energy-efficient communication backbones for massive multicore chips. The achievable performance of this type of on-chip interconnect infrastructure depends on the efficiency of the Media Access Control (MAC) protocol that arbitrates between the competing wireless nodes. In this work we propose the design of a distributed MAC protocol suitable for wireless NoC architectures. Compared to the widely used token passing scheme, a distributed MAC protocol improves scalability, provides better performance and lower overall energy dissipation. Depending on the traffic pattern, the proposed distributed MAC provides up to 23% improvement in energy delay product (EDP) when compared to the existing token passing scheme.
With its applicability spanning numerous data-driven fields, the implementation of graph analytics on multicore platforms is gaining momentum. One of the most important components of a multicore chip is its communication backbone. Due to inherent irregularities in data movements manifested by graph-based applications, it is essential to design efficient on-chip interconnection architectures for multicore chips performing graph analytics. In this article, we present a detailed analysis of the traffic patterns generated by graph-based applications when mapped to multicore chips. Based on this analysis, we explore the design-space for the Network-on-Chip (NoC) architecture to enable an efficient implementation of graph analytics. We principally consider three types of NoC architectures, viz., traditional mesh, small-world, and high-radix networks. We demonstrate that the small-world-network-enabled wireless NoC (WiNoC) is the most suitable platform for executing the considered graph applications. The WiNoC achieves an average of 38% and 18% full-system Energy Delay Product savings compared to wireline-mesh and high-radix NoCs, respectively.
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