High-speed single flux-quantum up/down counter for neural computation using stochastic logic

Abstract

High-speed single flux-quantum up/down counter for neural computation using stochastic logic Abstract. We propose the high-speed single flux-quantum(SFQ) up/down counter for the neural computation using stochastic logic. Up and down signals are counted by two independent counters, respectively. The proposed counter circuit can count SFQ signals of 50GHz with large operation margins. To realize the up/down counter, we fabricated a 2-bit up counter circuit and a 2-bit down counter circuit as basic blocks by Nb integrated circuits. The low-speed test results of these circuits show correct operations. We also measured dc voltages of Josephson transmission lines to investigate the responses of carry operations for SFQ pulse trains. The results show that the counter circuits have potential to count 144GHz SFQ pulses. IntroductionArtificial neural networks are promising for parallel and intelligent information processing. Implementation of a hardware neural network is an important subject for fields such as pattern recognition, optimization problems, and an adaptive learning. We have proposed superconducting neural circuits using stochastic logic [1]. The circuit elements have been based on a single fluxquantum(SFQ) logic[2], [3]. In the stochastic logic, the data is represented by the random pulse generation rate for a certain period on time domain. The neural computation must accumulate the pulses in order to generate the membrane potential of a neuron. We have already proposed the asynchronous up/down counter for accumulation of stochastic pulses. However, the operation speed of this counter is relatively slow(3.4GHz, 16bit) because of the slow operation in the reverse-count for the subtraction. Therefore, the operation speed of the up/down counter has limited the speed of the stochastic neuron circuit. In this paper, we propose a high-speed up/down counter for the accumulation of stochastic signals. Up and down signals are counted by two independent counters, respectively. After the signal accumulation, the final result is obtained by the subtraction between the counter of up signals and down signals. The up/down counter has two operation phases which are the accumulation of signals and the access of accumulation result. The numerical simulation shows that the up/down counter can operate at 50GHz with an enough bias margin in the accumulation phase. Even if the number of bit increases, the operation speed of the accumulation does not decrease in this method.

Pseudo Sigmoid Function Generator for a Superconductive Neural Network

“…Therefore, a superconductive single flux quantum (SFQ) circuit, which uses single flux quantum pulses as an information carrier [3], is suitable for implementing the ANN in principle. Superconductive ANNs have been proposed and implemented by several research institutes [4][5][6][7][8][9]. Superconductive stochastic logic, in which data is represented by the density of SFQ pulses in the time domain, has been proposed, and some circuit elements for superconductive ANNs have been implemented [8,9].…”

Multiplexing Techniques of Single Flux Quantum Circuit Based Readout Circuit for a Multi-Channel Sensing System

“…4 shows the block diagram of a 4-bit SFQ up/down counter. The up/down counter comprises adder cells and subtraction cells [13]. The internal state of the counter, which is incremented and decremented by the complementary inputs, is destructively output by inputting the readout clock.…”

Stochastic single flux quantum neuromorphic computing using magnetically tunable Josephson junctions

“…[4] Neuromorphic variations of SFQ logic have been proposed and fabricated. [5][6][7] These papers have highlighted the natural analogy between SFQ pulse trains and action potentials. However, key components of neural operation, such as neuromorphic synapses that are dynamically reconfigurable and system operation near the thermal limit, have not been addressed.…”