Energy-Efficient High-Order FIR Filtering through Reconfigurable Stochastic Processing (Abstract Only)

Abstract

High-order 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 implementing energy-efficient high-order FIR filtering through reconfigurable stochastic processing. We exploit a basic probabilistic principle of summing independent random variables to achieve approximate FIR filtering without costly multiplications. Our new multiplierless approach has two distinctive advantages when compared with the conventional multiplier-based or DA-based FIR filtering methods. First, our new probabilistic architecture is especially effective for high-order FIR filtering because it bypasses costly multiplications and does not rely on large size of memory to store store pre-computed coefficient products. Second, this new probabilistic convolver is significantly more robust or fault tolerant than the conventional architecture because all signal values will be represented and computed probabilistically, and local signal corruption can not easily destroy the overall probabilistic patterns, therefore achieving much higher error tolerance. For example, our proposed approach allows our proposed FIR architecture, for a standard 128-tap FIR filter, 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.