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Hyperdimensional computing (HDC) offers a singlepass learning system by imitating the brain-like signal structure. HDC data structure is in random hypervector format for better orthogonality. Similarly, in bit-stream processing – aka stochastic computing– systems, low-discrepancy (LD) sequences are used for the efficient generation of uncorrelated bit-streams. However, LD-based hypervector generation has never been investigated before. This work studies the utilization of LD Sobol sequences as a promising alternative for encoding hypervectors. The new encoding technique achieves highly-accurate classification with a single-time training step without needing to iterate repeatedly over random rounds. The accuracy evaluations in an embedded environment exhibit a classification rate improvement of up to 9.79% compared to the conventional random hypervector encoding.
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Massively parallel ultrafast random bit generation with a chip-scale laser
Random numbers are widely used for information security, cryptography, stochastic modeling, and quantum simulations. Key technical challenges for physical random number generation are speed and scalability. We demonstrate a method for ultrafast generation of hundreds of random bit streams in parallel with a single laser diode. Spatiotemporal interference of many lasing modes in a specially designed cavity is introduced as a scheme for greatly accelerated random bit generation. Spontaneous emission, caused by quantum fluctuations, produces stochastic noise that makes the bit streams unpredictable. We achieve a total bit rate of 250 terabits per second with off-line postprocessing, which is more than two orders of magnitude higher than the current postprocessing record. Our approach is robust, compact, and energy-efficient, with potential applications in secure communication and high-performance computation.
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- Award ID(s):
- 1953959
- PAR ID:
- 10215256
- Publisher / Repository:
- American Association for the Advancement of Science (AAAS)
- Date Published:
- Journal Name:
- Science
- Volume:
- 371
- Issue:
- 6532
- ISSN:
- 0036-8075
- Page Range / eLocation ID:
- p. 948-952
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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