Stochastic Computing

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## Motivation and a simple example

## History

## Strengths and weaknesses

### Strengths

### Weaknesses

## Stochastic decoding

## Deterministic Methods to Stochastic Computing

## Variants of stochastic computing

## References

## Further reading

This article uses material from the Wikipedia page available here. It is released under the Creative Commons Attribution-Share-Alike License 3.0.

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.

Suppose that is given, and we wish to compute . Stochastic computing performs this operation using probability instead of arithmetic.

Specifically, suppose that there are two random, independent bit streams called *stochastic number*s (i.e. Bernoulli processes), where the probability of a one in the first stream is , and the probability in the second stream is . We can take the logical AND of the two streams.

1 | 0 | 1 | 1 | 0 | 1 | ... | |

1 | 1 | 0 | 1 | 1 | 0 | ... | |

1 | 0 | 0 | 1 | 0 | 0 | ... |

The probability of a one in the output stream is . By observing enough output bits and measuring the frequency of ones, it is possible to estimate to arbitrary accuracy.

The operation above converts a fairly complicated computation (multiplication of and ) into a series of very simple operations (evaluation of ) on random bits.

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 and 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.

Stochastic computing was first introduced in a pioneering paper by John von Neumann in 1953.^{[1]} However, the
theory could not be fully developed until advances in computing of the 1960s,^{[2]}^{[3]}
mostly through a series of simultaneous and parallel efforts in the US^{[4]}
and the UK.^{[5]}
By the late 1960s, attention turned to the design of
special-purpose hardware to perform stochastic computation. A host^{[6]}
of these machines were constructed between 1969 and 1974; RASCEL^{[7]}
is pictured in this article.

Despite the intense interest in the 1960s and 1970s, stochastic
computing ultimately failed to compete with more traditional digital
logic, for reasons outlined below. The first (and last)
International Symposium on Stochastic Computing^{[8]}
took place in 1978; active research in the area dwindled over the next
few years.

Although stochastic computing declined as a general method of
computing, it has shown promise in several applications. Research has
traditionally focused on certain tasks in machine learning and
control.^{[9]}^{[10]}
Somewhat recently, interest has turned towards stochastic
decoding, which applies stochastic computing to the decoding of error
correcting codes.^{[11]} More recently, stochastic circuits have been successfully used in image processing tasks such as edge detection
^{[12]} and image thresholding.^{[13]}

Although stochastic computing was a historical failure, it may still remain relevant for solving certain problems. To understand when it remains relevant, it is useful to compare stochastic computing with more traditional methods of digital computing.

Suppose we wish to multiply two numbers each with bits of precision. Using the typical long multiplication method, we need to perform operations. With stochastic computing, we can AND together any number of bits and the expected value will always be correct. (However, with a small number of samples the variance will render the actual result highly inaccurate).

Moreover, the underlying operations in a digital multiplier are
full adders, whereas a stochastic
computer only requires an AND gate. Additionally,
a digital multiplier would naively require input wires,
whereas a stochastic multiplier would only require 2 input wires^{[]}.
(If the digital multiplier serialized its output, however, it would also
require only 2 input wires.)

Additionally, stochastic computing is robust against noise; if a few bits in a stream are flipped, those errors will have no significant impact on the solution.

Furthermore, stochastic computing elements can tolerate skew in the arrival time of the inputs.
Circuits work properly even when the inputs are misaligned temporally. As a result, stochastic
systems can be designed to work with inexpensive locally generated clocks instead of using a global clock and
an expensive clock distribution network.^{[14]}

Finally, stochastic computing provides an estimate of the solution
that grows more accurate as we extend the bit stream. In particular,
it provides a rough estimate very rapidly. This property is usually
referred to as *progressive precision*, which suggests that the precision
of stochastic numbers (bit streams) increases as computation proceeds.
^{[15]}
It is as if the most significant bits of the number
arrive before its least significant bits; unlike the
conventional arithmetic circuits where the most
significant bits usually arrive last. In some
iterative systems the partial solutions obtained through progressive precision can provide faster feedback
than through traditional computing methods, leading to faster
convergence.

Stochastic computing is, by its very nature, random. When we examine a random bit stream and try to reconstruct the underlying value, the effective precision can be measured by the variance of our sample. In the example above, the digital multiplier computes a number to bits of accuracy, so the precision is . If we are using a random bit stream to estimate a number and want the standard deviation of our estimate of the solution to be at least , we would need samples. This represents an exponential increase in work. In certain applications, however, the progressive precision property of stochastic computing can be exploited to compensate this exponential loss.

Second, stochastic computing requires a method of generating random biased bit streams. In practice, these streams are generated with pseudo-random number generators. Unfortunately, generating (pseudo-)random bits is fairly costly (compared to the expense of, e.g., a full adder). Therefore, the gate-level advantage of stochastic computing is typically lost.

Third, the analysis of stochastic computing assumes that the bit
streams are independent (uncorrelated). If this assumption does not
hold, stochastic computing can fail dramatically. For instance, if we
try to compute by multiplying a bit stream for
by itself, the process fails: since , the stochastic computation would yield , which is not generally true (unless 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.^{[16]}
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 . 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 require either stream decimation or inflation. Tradeoffs between precision and memory can be challenging.

Although stochastic computing has a number of defects when considered as a method of general computation, there are certain applications that highlight its strengths. One notable case occurs in the decoding of certain error correcting codes.

In developments unrelated to stochastic computing, highly effective methods of decoding LDPC codes using the belief propagation algorithm were developed. Belief propagation in this context involves iteratively reestimating certain parameters using two basic operations (essentially, a probabilistic XOR operation and an averaging operation).

In 2003, researchers realized that these two operations could be
modeled very simply with stochastic computing.^{[17]}
Moreover, since the
belief propagation algorithm is iterative, stochastic computing provides partial
solutions that may lead to faster convergence.
Hardware implementations of stochastic decoders have been built on FPGAs.
^{[18]}
The proponents of these methods argue that the performance of stochastic decoding is
competitive with digital alternatives.

Deterministic methods of SC has been developed to perform completely accurate computation with SC circuits.^{[19]} The essential principle of these methods is that every bit of one bit-streams interacts with every bit of the other bit-streams exactly once. To produce completely accurate result with these methods, the operation must run for the product of the length of input bit-streams. Deterministic methods are developed based on unary bit-streams,^{[20]}^{[21]} pseudo-random bit-streams,^{[22]} and low-discrepancy bit-streams.^{[23]}

There are a number of variants of the basic stochastic computing paradigm. Further information can be found in the referenced book by Mars and Poppelbaum.

*Bundle Processing* involves sending a fixed number of
bits instead of a stream. One of the advantages of this approach is
that the precision is improved. To see why, suppose we transmit
bits. In regular stochastic computing, we can
represent a precision of roughly different
values, because of the variance of the estimate. In bundle
processing, we can represent a precision of .
However, bundle processing retains the same robustness to error of
regular stochastic processing.

*Ergodic Processing* involves sending a stream of bundles, which
captures the benefits of regular stochastic and bundle processing.

*Burst Processing* encodes a number by a higher base increasing
stream. For instance, we would encode 4.3 with ten decimal digits as

- 4444444555

since the average value of the preceding stream is 4.3. This representation offers various advantages: there is no randomization since the numbers appear in increasing order, so the PRNG issues are avoided, but many of the advantages of stochastic computing are retained (such as partial estimates of the solution). Additionally, it retains the linear precision of bundle and ergodic processing.

**^**von Neumann, J. (1963). "Probabilistic logics and the synthesis of reliable organisms from unreliable components".*The Collected Works of John von Neumann*. Macmillan. ISBN 978-0-393-05169-8.**^**Petrovic, R.; Siljak, D. (1962). "Multiplication by means of coincidence".*ACTES Proc. of 3rd Int. Analog Comp. Meeting*.**^**Afuso, C. (1964),*Quart. Tech. Prog. Rept.*, Department of Computer Science, University of Illinois, Urbana, Illinois**^**Poppelbaum, W.; Afuso, C.; Esch, J. (1967). "Stochastic computing elements and systems".*Afips FJCC*.**31**: 635-644. doi:10.1145/1465611.1465696. ISBN 9781450378963. S2CID 8504153.**^**Gaines, B. (1967). "Stochastic Computing".*Afips SJCC*.**30**: 149-156. doi:10.1145/1465482.1465505. ISBN 9781450378956. S2CID 832296.**^**Mars, P.; Poppelbaum, W. (1981).*Stochastic and deterministic averaging processors*. P. Peregrinus. ISBN 978-0-906048-44-3.**^**Esch, John W. (1969).*RASCEL, a programmable analog computer based on a regular array of stochastic computing element logic*(PhD). University of Illinois, Urbana, Illinois. AAI700084.**^***Proceedings of the first International Symposium on Stochastic Computing and its Applications*. Toulouse, France. 1978. OCLC 499229066.**^**Gaines, B. R. (2013) [1969]. "Stochastic Computing Systems". In Tou, Julius (ed.).*Advances in Information Systems Science*.**2**. Springer. ISBN 9781489958433.**^**van Daalen, M.; Jeavons, P.; Shawe-Taylor, J. (1993). "A stochastic neural architecture that exploits dynamically reconfigurable FPGAs".*FPGAs for Custom Computing Machines, Proceedings IEEE, NAPA*. pp. 202-211. doi:10.1109/FPGA.1993.279462. ISBN 0-8186-3890-7.**^**Gaudet, Vincent; Rapley, Anthony (February 2003). "Iterative decoding using stochastic computation".*Electronics Letters*.**39**(3): 299-301. Bibcode:2003ElL....39..299G. doi:10.1049/el:20030217.**^**Alaghi, A.; Li, C.; Hayes, J. P. (2013). "Stochastic circuits for real-time image-processing applications".*Proceedings of the 50th Annual Design Automation Conference on - DAC '13*. p. 1. doi:10.1145/2463209.2488901. ISBN 9781450320719. S2CID 18174415.**^**Najafi, M. H.; Salehi, M. E. (2016). "A Fast Fault-Tolerant Architecture for Sauvola Local Image Thresholding Algorithm Using Stochastic Computing".*IEEE Transactions on Very Large Scale Integration (VLSI) Systems - TVLSI '16*.*IEEE Transactions on Very Large Scale Integration (VLSI) Systems*.**24**. pp. 808-812. doi:10.1109/TVLSI.2015.2415932. S2CID 6591306.**^**Najafi, M. H.; Lilja, D. J.; Riedel, M. D.; Bazargan, K. (2016).*Polysynchronous stochastic circuits*.*2016 21st Asia and South Pacific Design Automation Conference (ASP-DAC)*. pp. 492-498. doi:10.1109/ASPDAC.2016.7428060. ISBN 978-1-4673-9569-4. S2CID 8973285.**^**Alaghi, A.; Hayes, J. P. (2013). "Survey of Stochastic Computing".*ACM Transactions on Embedded Computing Systems*.**12**(2s): 1. CiteSeerX 10.1.1.296.4448. doi:10.1145/2465787.2465794. S2CID 4689958.**^**Winstead, C.; Rapley, A.; Gaudet, V.; Schlegel, C. (September 2005). "Stochastic iterative decoders".*IEEE International Symposium on Information Theory*. Adelaide Australia: 1116-1120. arXiv:cs/0501090. doi:10.1109/ISIT.2005.1523513. ISBN 0-7803-9151-9. S2CID 16390484.**^**Gaudet, Vincent; Rapley, Anthony (February 2003). "Iterative decoding using stochastic computation".*Electronics Letters*.**39**(3): 299-301. Bibcode:2003ElL....39..299G. doi:10.1049/el:20030217.**^**Gross, W.; Gaudet, V.; Milner, A. (2006). "Stochastic implementation of LDPC decoders".*Conference Record of the Thirty-Ninth Asilomar Conference on Signals, Systems and Computers*.**^**Najafi, M. Hassan; Jenson, Devon; Lilja, David J.; Riedel, Marc D. (December 2019). "Performing Stochastic Computation Deterministically".*IEEE Transactions on Very Large Scale Integration (VLSI) Systems*.**27**(12): 2925-2938. doi:10.1109/tvlsi.2019.2929354. ISSN 1063-8210. S2CID 201888463.**^**Jenson, Devon; Riedel, Marc (2016-11-07). "A deterministic approach to stochastic computation".*Proceedings of the 35th International Conference on Computer-Aided Design*. New York, NY, USA: ACM: 1-8. doi:10.1145/2966986.2966988. ISBN 978-1-4503-4466-1. S2CID 11281124.**^**Najafi, M. Hassan; Jamali-Zavareh, Shiva; Lilja, David J.; Riedel, Marc D.; Bazargan, Kia; Harjani, Ramesh (May 2017). "Time-Encoded Values for Highly Efficient Stochastic Circuits".*IEEE Transactions on Very Large Scale Integration (VLSI) Systems*.**25**(5): 1644-1657. doi:10.1109/tvlsi.2016.2645902. ISSN 1063-8210. S2CID 5672761.**^**Najafi, M. Hassan; Lilja, David (2018). "High Quality Down-Sampling for Deterministic Approaches to Stochastic Computing".*IEEE Transactions on Emerging Topics in Computing*: 1. doi:10.1109/tetc.2017.2789243. ISSN 2168-6750.**^**Najafi, M. Hassan; Lilja, David J.; Riedel, Marc (2018-11-05). "Deterministic methods for stochastic computing using low-discrepancy sequences".*Proceedings of the International Conference on Computer-Aided Design*. New York, NY, USA: ACM: 1-8. doi:10.1145/3240765.3240797. ISBN 978-1-4503-5950-4. S2CID 53236540.

- Gaines, Brian R. (1967). "Techniques of Identification with the Stochastic Computer" (PDF).
*Proceedings IFAC Symposium on "The Problems of Identification in Automatic Control Systems", Section 6 Special Identification Instruments, Prague June 12-19, 1967*. Retrieved . - Alaghi, Armin; Hayes, John P. (2013). "Survey of Stochastic Computing" (PDF).
*ACM Transactions on Embedded Computing Systems*.**12**(2s): 1-19. CiteSeerX 10.1.1.296.4448. doi:10.1145/2465787.2465794. S2CID 4689958. Retrieved .

This article uses material from the Wikipedia page available here. It is released under the Creative Commons Attribution-Share-Alike License 3.0.

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