Stochastic computing

Stochastic computing is a collection of techniques that represent continuous values by streams of random bits. Complex computations can then be computed by simple bit-wise operations on the streams. Stochastic computing is distinct from the study of randomized algorithms.

Stochastic computing is a collection of techniques that represent continuous values by streams of random bits. Complex computations can then be computed by simple bit-wise operations on the streams. Stochastic computing is distinct from the study of randomized algorithms. To put in another perspective, assuming the truth table of an AND gate. Conventional interpretation is that the output is true if and only if input A and B are true. However, if the table is interpreted vertically, (0011) AND (0101) is (0001), i.e., 1/2 x 1/2 = 1/4, which is exactly an arithmetic multiplication. As the information is presented in probability distribution, probability multiplication is literally an AND operation. More generally speaking, stochastic computing represents numbers as streams of random bits and reconstructs numbers by calculating frequencies. The computations are performed on the streams and translate complicated operations on p and q into simple operations on their stream representations. (Because of the method of reconstruction, devices that perform these operations are sometimes called stochastic averaging processors.) In modern terms, stochastic computing can be viewed as an interpretation of calculations in probabilistic terms, which are then evaluated with a Gibbs sampler. It can also be interpreted as a hybrid analog/digital computer. a_i=a_i , the stochastic computation would yield p \times p = p , which is not generally true (unless p= 0 or 1). In systems with feedback, the problem of decorrelation can manifest in more complicated ways. Systems of stochastic processors are prone to latching, where feedback between different components can achieve a deadlocked state. A great deal of effort must be spent decorrelating the system to attempt to remediate latching. Fourth, although some digital functions have very simple stochastic counterparts (such as the translation between multiplication and the AND gate), many do not. Trying to express these functions stochastically may cause various pathologies. For instance, stochastic decoding requires the computation of the function f(p,q)\rightarrow pq/(pq + (1-p)(1-q)) . There is no single bit operation that can compute this function; the usual solution involves producing correlated output bits, which, as we have seen above, can cause a host of problems. Other functions (such as the averaging operator f(p,q)\rightarrow (p+q)/2 require either stream decimation or inflation. Tradeoffs between precision and memory can be challenging. ==See also== Unconventional computing ==References== ==Further reading==

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