Reservoir computing is attracting great interest because of its high computing ability especially for time-series prediction, despite its simple structure and learning scheme. This paper proposes a reservoir computing hardware model using a chaotic Boltzmann machine (CBM) as the reservoir, which can achieve complex motion in a dynamical system on a high-dimensional hypercube. The CBM uses analog nonlinear dynamics, unlike the stochastic operation of the original Boltzmann machine model. To utilize CBMs as a reservoir, chaotic operation must be suppressed, and the echo state property should be satisfied. We modify the CBM model for simpler analog complementary metal-oxide-semiconductor very-large-scale integration (CMOS VLSI) implementation, and propose its use as a reservoir by adding an external reference clock signal. We then verify its proper operation by numerical simulation. We also refine the CMOS VLSI circuit design based on the proposed modified CBM model to improve power consumption and calculation precision.

This live demonstration presents a VLSI chip based on “Time-domain Analog Computing with Transient states (TACT)” approach for intelligent processing on robots. This TACT chip, fabricated using 250-nm CMOS technology, implements a time-domain analog weighted-sum calculation model with very high energy efficiency. We integrate the TACT chip into a robot via Robot Operating System (ROS) interfaces. A human tracking robot demonstration is performed by the TACT chip with energy efficiency of 300 TOPS/W.

This paper proposes CMOS VLSI implementation of a chaotic Boltzmann machine (CBM) model, which uses analog nonlinear dynamics instead of stochastic operation as in the original Boltzmann machine model. The CBM model is suitable for efficient VLSI implementation of Boltzmann machines because it requires no random number generator circuits, which consume a considerable footprint on a VLSI chip as well as considerable power. We describe the design results of CMOS circuits of neuron and synapse units. The neuron circuit uses subthreshold operation of MOSFETs to realize the exponential function used in the CBM model. We also provide measurement results of a fabricated CMOS chip for single-neuron unit circuit operation and demonstrate chaotic behavior in a three-neuron network.

In this paper, we propose a crossover method to improve a GA-based Q-learning method for controlling multi-legged robots. As a GA-based Q-learning method, we employ a method called "Q-learning with Dynamic Structuring of Exploration Space Based on Genetic Algorithm (QDSEGA)". We propose a crossover for QDSEGA, and a method to reward a robot in Q-learning in order to follow a moving target. Simulation results clearly show the effectiveness of the proposed methods.

The use of quorums is a well-known approach to achieving mutual exclusion in distributed computing systems. This approach works based on a coterie, a special set of node groups where any pair of the node groups shares at least one common node. Each node group in a coterie is called a quorum. Mutual exclusion is ensured by imposing that a node gets consensus from all nodes in at least one of the quorums before it enters a critical section. In a quorum-based mutual exclusion scheme, the delay for reaching consensus depends critically on the coterie adopted and, thus, it is important to find a coterie with small delay. Fu (1997) introduced two related measures called max-delay and mean-delay. The former measure represents the largest delay among all nodes, while the latter is the arithmetic mean of the delays. She proposed polynomial-time algorithms for finding max-delay and mean-delay optimal coteries when the network topology is a tree or a ring. In this paper, we first propose a polynomial-time algorithm for finding max-delay optimal coteries and, then, modify the algorithm so as to reduce the mean-delay of generated coteries. Unlike the previous algorithms, the proposed algorithms can be applied to systems with arbitrary topology.