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Following technology scaling, on-chip heterogeneous architecture emerges as a promising solution to combat the power wall of microprocessors. This work presents <bold>Harmonica</bold>—aframework of heterogeneous computing system enhanced by memristor-based neuromorphic computing accelerators (NCAs). In Harmonica, a conventional pipeline is augmented with a NCA which is designed to speedup...
Energy consumption is the key indicator of speed control technology for improving energy efficiency of belt conveyor systems. This paper presents the design and verification of a short-term mathematical model intended for the prediction of energy consumption of a belt conveyor system using neural network method. The obtained model corresponds with real operational conditions. It can take advantage...
As conventional CMOS technology approaches fundamental scaling limits novel nanotechnologies offer great promise for VLSI integration at nanometer scales. The memristor, or memory resistor, is a novel nanoelectronic device that holds great promise for continued scaling for emerging applications. Memristor behavior is very similar to that of the synapses necessary for realizing a neural network. In...
A robust fault diagnosis (FD) scheme using Takagi-Sugeno (T-S) neural-fuzzy model and sliding mode technique is presented for a class of nonlinear systems that can be described by T-S fuzzy models. A neural-fuzzy observer and neural-fuzzy sliding mode observer are constructed respectively. A modified back-propagation (BP) algorithm is used to update the parameters of the two observers. Stability of...
Cogent confabulation is a computation model that mimics the Hebbian learning, information storage, inter-relation of symbolic concepts, and the recall operations of the brain. The model has been applied to cognitive processing of language, audio and visual signals. In this project, we focus on how to accelerate the computation which underlie confabulation based sentence completion through software...
A robust fault diagnosis (FD) scheme integrating Takagi-Sugeno (T-S) fuzzy-neural models and sliding mode technique is presented for a class of nonlinear systems that can be described by T-S fuzzy models. A fuzzy-neural observer and a fuzzy-neural sliding mode observer are constructed respectively. A modified back-propagation (BP) algorithm is used to update the parameters of these two observers....
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