Early Termination with Activation Sign Prediction for Energy-Efficient CNN Inference Using Sum-of-Power-of-Two Quantization

We propose two techniques to optimize CNN inference for both energy efficiency and accuracy. First, Sum-of-Power-of-Two (SPoT) quantization enhances logarithmic quantization by enabling shift-based multiplications with reduced accuracy loss. Second, activation sign prediction enables early termination by estimating pre-activation signs from the most significant power-of-two terms and skipping computations that ReLU would zero out. To validate these methods, we design a processing element (PE) with a shift-based MAC unit integrating SPoT and early termination.