Neural Network Accelerator Array for Low-Power Parallel Processing

The neural network hardware is segmented into multiple computing elements arranged in an array, where each computing element processes a portion of the neural network computations. This segmentation allows the system to achieve high processing throughput through parallel execution while keeping individual computing element power consumption low, thereby resolving the contradiction between processing speed and power consumption.

Each computing element in the array is designed to be reconfigurable and can perform multiple types of neural network operations (e.g., matrix multiplication, convolution, activation functions). This multi-functionality allows a fixed hardware architecture to efficiently handle diverse neural network workloads without requiring separate dedicated hardware for each operation, reducing overall hardware requirements while maintaining high processing capability.

The computing elements are designed with dynamic reconfiguration capabilities through firmware or software control, allowing the hardware to adapt to different neural network architectures and workloads. The reconfiguration is achieved by modifying operational parameters and data flow patterns rather than physically rearranging hardware components, which maintains relatively simple hardware structure while providing high adaptability.

Instead of creating physically different hardware for each neural network application, the system uses software-based configuration to create virtual copies of computing element behaviors. Each computing element can be programmed to emulate different neural network layer types through firmware, achieving application-specific optimization without increasing physical hardware complexity.