FIR Filter Based on Stochastic Computing with Reconfigurable Digital Fabric

FIR filtering is widely used in many important DSP applications in order to achieve filtering stability and linear-phase property. This paper presents a hardware- and energy-efficient approach to implement FIR filtering through reconfigurable stochastic computing. Specifically, we exploit a basic probabilistic principle of summing independent random variables to achieve approximate FIR filtering without costly multiplications. This allows our proposed FIR architecture to achieve about 9 times and 4 times less power consumption than the conventional multiplier-based and DA-based design, respectively. Additionally, when compared with the state-of-the-art systolic DA-based design, our design can achieve about 3 times reduction in hardware usage.