Customized hardware architecture design is fundamental to optimizing accelerator utilization. Traditional general-purpose computing architectures struggle to efficiently meet the diverse computational demands of neural networks, while specialized architectures such as RNA and DNA offer targeted solutions for different scenarios. The RNA architecture dynamically reconfigures hardware resources, supporting floating-point, mixed-signal, and event-driven computing modes to flexibly adapt to the computational needs of general-purpose neural networks. The DNA architecture, specifically designed for convolutional neural networks, employs reconfigurable data paths and parallel convolutional mapping methods to achieve mixed data reuse, significantly improving computational resource utilization. This architectural-level customization allows accelerators to more accurately match the computational characteristics of neural networks, reducing resource idleness.
Optimization of AI computing module computation modes can further enhance accelerator execution efficiency. In neural network computation, matrix and vector operations dominate, and specialized hardware such as GPUs and TPUs significantly improve the throughput of these operations through parallel computing cores and tensor processing units. For example, TPUs employ a pulsating array architecture, keeping weight data stationary as it flows between computing units, reducing data transfer overhead; GPUs, on the other hand, support mixed-precision computing through Tensor Core technology, achieving higher energy efficiency in 16-bit floating-point operations. These innovative computing models enable accelerators to handle more tasks with the same hardware resources, improving overall utilization.
Improving storage efficiency in AI computing modules is crucial for optimizing accelerator utilization. In neural network computations, frequent access to weight data and intermediate results makes storage bandwidth a performance bottleneck. RANA, a storage optimization framework based on data retention time, introduces high-density eDRAM storage technology, combined with hybrid computing scheduling and refresh mechanisms, reducing off-chip memory accesses and system power consumption. This framework leverages the fault tolerance of neural network algorithms, allowing eDRAM to temporarily store data with almost no refresh, significantly reducing the storage subsystem's consumption of computing resources and freeing up more hardware resources for actual computing tasks.
Hardware-software co-design is key to unlocking the potential of accelerators. The performance of hardware accelerators relies heavily on software-layer optimization. For example, automatic parallelization techniques can automatically generate efficient parallel computing schemes based on the neural network model structure and hardware resources, reducing manual optimization costs. Sparse computing techniques utilize zero-value elements in the neural network to skip invalid computations, improving resource utilization. Furthermore, software framework support for mixed-precision computing and distributed training can further unleash hardware acceleration capabilities, forming a closed-loop optimization mechanism that integrates software and hardware.
The appropriate application of parallel computing strategies can significantly improve accelerator throughput. Data parallelism divides training data into multiple processing units, each independently computing and then aggregating gradients, suitable for training large-scale datasets. Model parallelism splits the neural network model into different processing units, transmitting intermediate results via communication, resolving the dependence of ultra-large-scale models on single-device resources. Combining asynchronous communication and compressed communication data to optimize communication overhead during parallel processing can prevent communication latency from becoming a performance bottleneck, ensuring accelerator resources are continuously utilized efficiently.
Optimizing the utilization of neural network accelerators in the AI computing module requires a multi-dimensional approach, encompassing hardware architecture, computing models, storage efficiency, software-hardware collaboration, and parallel computing. Through customized hardware design, innovative computing models, storage optimization, hardware-software synergy, and efficient parallel strategies, the adaptability and execution efficiency of accelerators for neural network tasks can be significantly improved, providing high-performance, low-power computing support for artificial intelligence applications.
