具有低功耗硬件实现的神经形态伪随机数生成器
Neuromorphic Pseudo-Random Number Generators with a Low Power Hardware Implementation
- Hotchkiss Brain Institute, University of Calgary（卡尔加里大学霍奇金斯脑研究所）
- Biomedical Engineering, University of Calgary（卡尔加里大学生物医学工程系）
- Electrical and Software Engineering, University of Calgary（卡尔加里大学电气与软件工程系）
- Creative Destruction Lab（创意破坏实验室）
- ACHRI, University of Calgary（卡尔加里大学应用计算健康研究 institute）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
该研究利用大脑混沌平衡状态的计算模型，构建了基于漏积分发放神经元的平衡尖峰神经网络，在低功耗FPGA上实现了神经形态伪随机数生成器，功耗3.24mW，速率120kbps，并通过标准质量测试。
AI中文摘要：
伪随机数生成通常需要在质量、功耗和带宽之间进行权衡，以产生不可预测的数字序列。另一方面，大脑通过高维状态中发生的复杂网络动力学高效地产生不可预测的输出。这种状态被假设为混沌的，依赖于兴奋和抑制之间的平衡。在此，我们研究了这些混沌平衡状态的计算模型是否可用于低功耗硬件中的神经形态伪随机数生成器（NPRNG）。我们成功构建了一个由漏积分发放神经元组成的平衡尖峰神经网络模型，该模型可以轻松地在低功耗FPGA中实现并用作NPRNG。原型NPRNG在运行期间消耗3.24 mW，并以120kbps的速度产生伪随机数。在硬件和软件实例中，NPRNG产生高质量的随机数，并通过了用于测试RNG质量的标准指标验证。
英文摘要：
Pseudo-random number generation often requires trade-offs among quality, power consumption, and bandwidth to produce unpredictable sequences of numbers. The brain, on the other hand, efficiently generates unpredictable output complex network dynamics occurring in a high-dimensional state. This state, which is hypothesized to be chaotic, relies on the balance between excitation and inhibition. Here, we investigated if computational models of these chaotic balanced states can be harnessed for Neuromorphic Pseudo-Random Number Generators (NPRNGs) in low power hardware. We successfully constructed a balanced spiking neural network model consisting of leaky-integrate-and-fire neurons that could be readily implemented in low power FPGAs and used as a NPRNG. The prototyped NPRNG consumed 3.24 mW during operation and produced pseudo-random numbers at 120kbps. In both hardware and software instantiations, NPRNGs produce high-quality random numbers as validated by standard metrics for testing RNG quality.
