Distributed Neural Network Acceleration with DMA Buffer Memory

The system segments the neural network processing into discrete functional blocks that can be implemented using standard, cost-effective hardware components rather than expensive specialized GPGPU hardware. By distributing computation across multiple simple processing elements connected through standardized interfaces, the patent achieves neural network acceleration using commodity hardware, significantly reducing both initial and maintenance costs compared to GPGPU-based solutions.

The processing elements in the patent are designed to be universal and reconfigurable, capable of handling different neural network layers and operations (convolution, activation, pooling, fully-connected). This multi-functionality allows the same hardware architecture to be used for various neural network models and applications, eliminating the need for expensive specialized hardware for each specific task and enabling cost-effective deployment across diverse scenarios.

The patent implements segmentation of neural network computation across multiple low-power processing elements, each handling specific computational stages. This distributed architecture enables the system to be deployed in low-power environments such as IoT devices, where individual processing elements consume minimal power while collectively achieving the required computational performance for neural network inference and training tasks.

The system employs dynamic resource allocation and configuration, allowing processing elements to be activated or deactivated based on computational requirements. This dynamic behavior enables the architecture to adapt to varying power constraints and performance demands, making it suitable for both low-power IoT applications and higher-performance scenarios by simply adjusting which processing elements are active.