Loop-intensive computations dominate embedded DSP applications, making loop branch prediction critical for maintaining pipeline efficiency. However, a gap exists between resource-heavy dynamic predictors and inflexible Zero Overhead Looping (ZOL) hardware.
Conventional predictors are often too costly for embedded silicon, while traditional ZOL is limited by strict constraints on loop size, nesting, and data-dependent exit conditions. To address these limitations, we introduce LoopHint, a hardware-software co-design approach that provides a flexible, learning-free loop prediction mechanism. LoopHint utilizes compiler-time static analysis to identify deterministic loop patterns and dependency chains, communicating this metadata to a simplified hardware loop table via specialized instructions. This co-design approach allows the processor to bypass the warm-up and aliasing issues of dynamic predictors while supporting a broader range of loop structures than ZOL. Experimental results on DSP kernels and the Embench-IoT benchmark suite show that LoopHint achieves an average 67% reduction in branch mispredictions. With minimal impact on the area and power, LoopHint provides a scalable solution for high-efficiency control flow in modern embedded DSPs.