Near-Optimal Hardware Design for Convolutional Neural Networks (2002.05526v1)

Abstract: Recently, the demand of low-power deep-learning hardware for industrial applications has been increasing. Most existing AI chips have evolved to rely on new chip technologies rather than on radically new hardware architectures, to maintain their generality. This study proposes a novel, special-purpose, and high-efficiency hardware architecture for convolutional neural networks. The proposed architecture maximizes the utilization of multipliers by designing the computational circuit with the same structure as that of the computational flow of the model, rather than mapping computations to fixed hardware. In addition, a specially designed filter circuit simultaneously provides all the data of the receptive field, using only one memory read operation during each clock cycle; this allows the computation circuit to operate seamlessly without idle cycles. Our reference system based on the proposed architecture uses 97% of the peak-multiplication capability in actual computations required by the computation model throughout the computation period. In addition, overhead components are minimized so that the proportion of the resources constituting the non-multiplier components is smaller than that constituting the multiplier components, which are indispensable for the computational model. The efficiency of the proposed architecture is close to an ideally efficient system that cannot be improved further in terms of the performance-to-resource ratio. An implementation based on the proposed hardware architecture has been applied in commercial AI products.

• The paper proposes a novel, high-efficiency hardware architecture for convolutional neural networks (CNNs) that maximizes resource utilization by aligning circuit design with CNN data flow.
• The architecture achieves 97% multiplier utilization through an innovative memory and filter design, significantly outperforming general-purpose AI hardware like NVIDIA Xavier (5%) and Google Coral (4.1%).
• This research offers a compelling solution for low-power, high-speed embedded AI applications and suggests a paradigm shift towards specialized hardware for specific AI workloads.

Near-Optimal Hardware Design for Convolutional Neural Networks

The paper "Near-Optimal Hardware Design for Convolutional Neural Networks" by Byungik Ahn proposes a high-efficiency hardware architecture tailored for convolutional neural network (CNN) computations, aimed at addressing the growing demand for low-power deep-learning hardware. This work deviates from the typical approach of leveraging advancements in chip technology and instead introduces a specialized architecture that maximizes resource efficiency, specifically targeting industrial applications where CNNs have become fundamental.

Summary of Proposed Architecture

The paper presents a novel hardware architecture that aligns the computational circuit design with the data flow of the CNN computational model. Notably, the architecture utilizes a neuron machine framework. A key feature of this approach is the maximization of multiplier utilization, achieving 97% of peak-multiplication capability through an innovative memory and filter design. By reducing the role of non-multiplier components, the system approaches an efficiency that borders on ideal, minimizing idle cycles and resource wastage.

The hardware is composed of fully pipelined circuits that enable continuous computation, supported by a distributed memory system. The filter circuit is designed to supply the entire receptive field data in a single memory read per clock cycle, ensuring a seamless flow of operations. This architecture contrasts with general-purpose designs like NVIDIA's Xavier and Google's Coral Edge, which show much lower utilization rates of 5% and 4.1%, respectively. By focusing on CNN-specific computations, the proposed architecture simplifies the system and significantly boosts efficiency.

Efficiency Metrics and Implementation

Ahn introduces two critical metrics for evaluating system efficiency: the multiplier composition efficiency ($R_c$) and the multiplier utilization efficiency ($R_u$). These metrics quantify the percentage of total resources used for multipliers and the actual versus peak multiplication speed, respectively. The reference implementation of the architecture on an FPGA demonstrates an effective utilization rate ($R_u$) of 97.2%. This implementation outperforms existing AI solutions, showcasing the potential for significantly improved performance in semiconductor-limited environments.

The implementation achieves a multiplication efficiency ($R_u \times R_c$) of 0.533, reflecting the limited contribution of non-multiplier components in the overall resource consumption. This positions the architecture close to optimal efficiency, a remarkable achievement given the constraints of current hardware technologies.

Practical and Theoretical Implications

The implications of this research extend to both practical and theoretical domains in AI and hardware design. Practically, the architecture's efficiency offers a compelling solution for embedded AI applications requiring low-power consumption and high-speed processing, such as in CCTV cameras and autonomous vehicles. From a theoretical perspective, the work elucidates the benefits of aligning hardware design with computational models, suggesting a paradigm shift towards specialized architectures for specific AI workloads.

Future Directions

While the paper focuses on inference, the principles outlined could be extended to training architectures, which rely heavily on backpropagation. The proposed architecture has already been realized in commercial products, such as Neurocoms's Deep Runner, indicating the design's viability and readiness for deployment. Future research may explore adapting the architecture for emerging technologies like quantum computing, which could further augment computation efficiencies.

In conclusion, this paper introduces a specialized hardware design for CNNs, yielding unprecedented efficiency in resource utilization. As AI applications continue to proliferate, the relevance of such tailored hardware solutions is likely to grow, necessitating continued exploration and adaptation to new computational paradigms.