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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, parallel and iteration, are presented. The power...
We present the design and measurement of a continuous-time, accelerated, reconfigurable Leaky Integrate and Fire (LIF) neuron model emulated in 65-nm CMOS technology. The neuron circuit is designed as a sub-circuit of our highly integrated neuromorphic prototype chip, the “HICANN-DLS”. The design is geared towards testability and debug features, as well as area and power efficiency. Each neuron in...
We present the design and measurement of a continuous-time, accelerated, reconfigurable Leaky Integrate and Fire (LIF) neuron model emulated in 65-nm CMOS technology. The neuron circuit is designed as a sub-circuit of our highly integrated neuromorphic prototype chip, the “HICANN-DLS”. The design is geared towards testability and debug features, as well as area and power efficiency. Each neuron in...
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 arise from correlations between spike times,...
Silicon neuron circuits emulate the electrophysiological behavior of real neurons. Many circuits can be integrated on a single very large scale integration (VLSI) device, and form large networks of spiking neurons. Connectivity among neurons can be achieved by using time multiplexing and fast asynchronous digital circuits. As the basic characteristics of the silicon neurons are determined at design...
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