3 research outputs found
Unsupervised SFQ-Based Spiking Neural Network
Single Flux Quantum (SFQ) technology represents a groundbreaking advancement
in computational efficiency and ultra-high-speed neuromorphic processing. The
key features of SFQ technology, particularly data representation, transmission,
and processing through SFQ pulses, closely mirror fundamental aspects of
biological neural structures. Consequently, SFQ-based circuits emerge as an
ideal candidate for realizing Spiking Neural Networks (SNNs). This study
presents a proof-of-concept demonstration of an SFQ-based SNN architecture,
showcasing its capacity for ultra-fast switching at remarkably low energy
consumption per output activity. Notably, our work introduces innovative
approaches: (i) We introduce a novel spike-timing-dependent plasticity
mechanism to update synapses and to trace spike-activity by incorporating a
leaky non-destructive readout circuit. (ii) We propose a novel method to
dynamically regulate the threshold behavior of leaky integrate and fire
superconductor neurons, enhancing the adaptability of our SNN architecture.
(iii) Our research incorporates a novel winner-take-all mechanism, aligning
with practical strategies for SNN development and enabling effective
decision-making processes. The effectiveness of these proposed structural
enhancements is evaluated by integrating high-level models into the BindsNET
framework. By leveraging BindsNET, we model the online training of an SNN,
integrating the novel structures into the learning process. To ensure the
robustness and functionality of our circuits, we employ JoSIM for circuit
parameter extraction and functional verification through simulation
Design of a Superconducting Multiflux Non-Destructive Readout Memory Unit
Due to low power consumption and high-speed performance, superconductor
circuit technology has emerged as an attractive and compelling post-CMOS
technology candidate. However, the design of dense memory circuits presents a
significant challenge, especially for tasks that demand substantial memory
resources. While superconductor memory cells offer impressive speed, their
limited density is the primary yet-to-be-solved challenge. This study tackles
this challenge head-on by introducing a novel design for a Non-Destructive
Readout (NDRO) memory unit with single or multi-fluxon storage capabilities
within the same circuit architecture. Notably, single storage demonstrates a
critical margin exceeding 20\%, and multi-fluxon storage demonstrates 64\%,
ensuring reliable and robust operation even in the face of process variations.
These memory units exhibit high clock frequencies of 10GHz. The proposed
circuits offer compelling characteristics, including rapid data propagation and
minimal data refreshment requirements, while effectively addressing the density
concerns associated with superconductor memory, doubling the memory capacity
while maintaining the high throughput speed.Comment: 6 pages, 11 figure
An On-Chip Trainable Neuron Circuit for SFQ-Based Spiking Neural Networks
We present an on-chip trainable neuron circuit. Our proposed circuit suits
bio-inspired spike-based time-dependent data computation for training spiking
neural networks (SNN). The thresholds of neurons can be increased or decreased
depending on the desired application-specific spike generation rate. This
mechanism provides us with a flexible design and scalable circuit structure. We
demonstrate the trainable neuron structure under different operating scenarios.
The circuits are designed and optimized for the MIT LL SFQ5ee fabrication
process. Margin values for all parameters are above 25\% with a 3GHz throughput
for a 16-input neuron.Comment: 5 pages, 8 figures. The work was presented in EUCAS 202