Novel Noise-Shaping Stochastic-Computing Converters for Digital Filtering
Kleanthis Papachatzopoulos, Chris Andriakopoulos, Vassilis Paliouras · 2020
Stochastic computing introduces massive parallelism in several practical applications by utilizing minimal-complexity processing elements, and provides inherent fault-tolerant features. However stochastic computation systems require long bit streams to achieve sufficient performance in terms of Signal to Noise Ratio. This paper proposes a first- and a second-order Noise-Shaping Binary-to-Stochastic Converter (NSBSC) for bipolar format. The proposed architecture schemes are compared with a baseline Binary-to-Stochastic Converter (BSC) in terms of Signal-to-Quantization-Noise Ratio (SQNR). It is shown that for certain test cases, the proposed architecture leads to 15.276 dB improved SQNR for the same bit stream length and, furthermore, achieves the same SQNR as the conventional converter using as much as 93.75% shorter stream lengths. Furthermore, the analysis includes area and power figures for the introduced hardware architectures for a 28-nm FDSOI technology. Finally, achieved NSBSC gains are shown to propagate at the output of a stochastic FIR filter, proving that the stochastic properties of the derived stream are maintained.