Stochastic Computing
Stochastic computing is a computational paradigm that represents and processes data using random bit streams, where the probability of a '1' in the stream encodes a numerical value. It enables low-precision arithmetic operations like addition and multiplication using simple logic gates, making it energy-efficient and fault-tolerant. This approach is particularly useful in applications where approximate computing is acceptable, such as in neural networks, signal processing, and error-prone environments.
Developers should learn stochastic computing when working on hardware-constrained systems, such as IoT devices or edge computing, where energy efficiency and resilience to noise are critical. It's valuable for implementing probabilistic algorithms, machine learning inference, and digital signal processing with reduced hardware complexity. Use cases include low-power AI chips, biomedical sensors, and radiation-hardened electronics for space applications.