Configurable CNN Processor Serial Link Architecture

The processor is divided into multiple specialized processing elements (PEs), each dedicated to specific CNN operations. These PEs are organized in a modular architecture where each unit handles particular computational tasks (e.g., convolution, activation functions) without requiring general-purpose load/store operations, thereby reducing power consumption while maintaining real-time processing capability.

The processing elements are designed with integrated data paths and internal memory structures that allow them to operate autonomously for CNN computations. The architecture enables data to flow directly between PEs through dedicated communication links, eliminating the need for external memory access and traditional load/store instructions, thus improving efficiency and reducing power usage.

The architecture implements a hierarchical memory structure where small on-chip memory buffers are nested within each processing element, which are in turn nested within larger memory structures. This nested arrangement allows frequently accessed data to be stored in fast on-chip memory, eliminating the need for slow external memory access while maintaining the capacity to handle large datasets through coordinated memory hierarchy management.

On-chip memory buffers and data redistribution networks serve as intermediaries between the processing elements and external memory. These intermediaries pre-fetch and cache data needed by PEs, allowing computations to proceed without waiting for slow external memory access, thereby significantly reducing memory access time and improving processing speed.

The processing elements are designed with configurable parameters and programmable logic that allow them to adapt to different CNN architectures. Each PE can be dynamically configured to perform various operations (e.g., different convolution kernel sizes, activation functions, pooling operations) through control registers and configuration interfaces. This universal design maintains high processing efficiency for specific CNN models while enabling flexibility to support multiple different network architectures.

The architecture implements dynamic reconfigurability where processing elements can change their operational characteristics during runtime based on the requirements of different CNN models. Configuration parameters such as data precision, computation precision, and operational mode can be dynamically adjusted to optimize performance for specific tasks while maintaining the ability to adapt to new CNN architectures, thus balancing specialization with versatility.