Chenyuan Zhao

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Reservoir Computing (RC) is a recent neurologically inspired concept for processing time dependent data that lends itself particularly well to hardware implementation by using the device physics to conduct information processing. In this paper, we apply RC to channel estimation in Orthogonal Frequency Division Multiplexing (OFDM) systems. Due to the(More)
This article presents our research towards developing novel and fundamental methodologies for data representation using spike-timing-dependent encoding. Time encoding efficiently maps a signal's amplitude information into a spike time sequence that represents the input data and offers perfect recovery for band-limited stimuli. In this article, we pattern(More)
Neuromorphic computing is a novel paradigm that inspired from the dynamic behavior of the biological brain. The encoding capability plays a vital role in information processing, especially for neural network based systems. In this paper, a compact, low power, and robust spiking-time-dependent encoder is designed with an accommodative Leaky Integrate and(More)
Neural encoder is one of the key components in neuromorphic computing systems, whereby sensory information is transformed into spike coded trains. The design of temporal encoder has attracted a widespread attention in the field of neuromorphic computing in the past few years. The information in the temporal encoding scheme with inter-spike intervals can(More)
With the emerging cutting-edge semiconductor nanotechnologies, reservoir computing has shifted the focus from software implementation towards hardware and optical implementations over the last few decades. Nowadays, in the field of reservoir computing, pure analog implementation with the employment of CMOS nanotechnology has attracted a worldwide attention.(More)
Designing power amplifiers with low power consumption, high efficiency and integration is an important topic with significant impact on communication and circuit research areas. In order to make transceivers more powerful with lower cost and higher integration, a CMOS power amplifier working from 3.5 GHz to 4.5 GHz is proposed. Cascode driver stage is(More)
Neuromorphic computing hardware has undergone a rapid development and progress in the past few years. One of the key components in neuromorphic computing systems is the neural encoder which transforms sensory information into spike trains. In this paper, both rate encoding and temporal encoding schemes are discussed. Two novel temporal encoding schemes,(More)
A new high performance CMOS bandgap reference (BGR) is designed and implemented in 0.18um CMOS technology. The proposed BGR circuit uses weak inversion technology to generate complementary-to-absolute temperature current and add the proportional-to-absolute temperature current into a transistor to generate the reference voltage. The output reference voltage(More)
Von Neumann bottleneck, which refers to the limited throughput between the CPU and memory, has already become a major factor hindering the technical advances of computing systems. In recent years, neuromorphic systems have started to gain the increasing attentions as compact and energy-efficient computing platforms. As one of the most crucial components in(More)
Making a computing system that mimic biological neural behavior in mammalian brain has attracted worldwide attention and endeavor. Neuromorphic computing systems, employing very-large-scale integration circuits to implement onto hardware, incorporates learning. Neural encoder, as one of the crucial component in neuromorphic computing systems, encodes the(More)