FPGA Random Number Generator
August 16, 2022 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Jacob Hammond
arXiv ID
2209.04423
Category
cs.AR: Hardware Architecture
Cross-listed
cs.CR
Citations
2
Venue
arXiv.org
Last Checked
3 months ago
Abstract
Random number generation is a key technology that is useful in a variety of ways. Random numbers are often used to generate keys for data encryption. Random numbers generated at a sufficiently long length can encrypt sensitive data and make it difficult for another computer or person to decrypt the data. Other uses for random numbers include statistical sampling, search/sort algorithms, gaming, and gambling. Due to the wide array of applications for random numbers, it would be useful to create a method of generating random numbers reliably directly in hardware to generate a ready supply of a random number for whatever the end application may be. This paper offers a proof-of-concept for creating a verilog-based hardware design that utilizes random measurement and scrambling algorithms to generate 32-bit random synchronously with a single clock cycle on a field-programmable-gate-array(FPGA).
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Hardware Architecture
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Corona: System Implications of Emerging Nanophotonic Technology
R.I.P.
π»
Ghosted
A scalable multi-core architecture with heterogeneous memory structures for Dynamic Neuromorphic Asynchronous Processors (DYNAPs)
R.I.P.
π»
Ghosted
SpAtten: Efficient Sparse Attention Architecture with Cascade Token and Head Pruning
R.I.P.
π»
Ghosted
Neural Cache: Bit-Serial In-Cache Acceleration of Deep Neural Networks
R.I.P.
π»
Ghosted
SpArch: Efficient Architecture for Sparse Matrix Multiplication
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted
Explanation in Artificial Intelligence: Insights from the Social Sciences
R.I.P.
π»
Ghosted