FFT DSP Engine with Feedback Architecture for Low-Latency Processing

The patent implements a unified hardware architecture that can perform both FFT operations for digital signal processing and matrix multiplication operations for machine learning. The same computational units (multipliers, accumulators, data paths) are configured to execute different algorithms based on control signals, eliminating the need for separate specialized circuitry for each domain while maintaining high performance in both applications.

The patent implements a feedback structure where intermediate results from FFT operations are retained within the hardware accelerator's internal memory or registers. This allows subsequent FFT operations to reuse previously computed data without requiring external read/write operations, thereby reducing latency and power consumption while maintaining processing accuracy through preserved computational precision.

The patent employs a feedback mechanism where the hardware accelerator monitors operational patterns and retains data in internal storage when subsequent operations require the same data. This feedback-controlled data retention strategy reduces frequent external memory access, thereby lowering power consumption while ensuring data freshness is maintained through controlled caching of intermediate results.