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Found 8 papers, 2 papers with code
On Leaky-Integrate-and Fire as Spike-Train-Quantization Operator on Dirac-Superimposed Continuous-Time Signals
Leaky-integrate-and-fire (LIF) is studied as a non-linear operator that maps an integrable signal $f$ to a sequence $\eta_f$ of discrete events, the spikes.
SNN Architecture for Differential Time Encoding Using Decoupled Processing Time
We present synthesis and performance results showing that this architecture can be implemented for networks of more than 1000 neurons with high clock speeds on a State-of-the-Art FPGA.
Quantization in Spiking Neural Networks
In spiking neural networks (SNN), at each node, an incoming sequence of weighted Dirac pulses is converted into an output sequence of weighted Dirac pulses by a leaky-integrate-and-fire (LIF) neuron model based on spike aggregation and thresholding.
Spiking Neural Networks in the Alexiewicz Topology: A New Perspective on Analysis and Error Bounds
A central question is the adequate structure for a space of spike trains and its implication for the design of error measurements of SNNs including time delay, threshold deviations, and the design of the reinitialization mode of the leaky-integrate-and-fire (LIF) neuron model.
A Fiber Measurement System with Approximate Deconvolution Based on the Analysis of Fault Clusters in Linearized Bregman Iterations
Automatic detection of faults in optical fibers is an active area of research that plays a significant role in the design of reliable and stable optical networks.
Efficient Majority Voting in Digital Hardware
In this work, we present a novel architecture that allows obtaining a majority decision in a number of clock cycles that is logarithmic in the number of inputs.
Efficient Non-sequential Division for FPGAs
Especially considering today's demand for hardware accelerators for machine learning algorithms, there is a high demand for an efficient calculation of the division function, e. g. for averaging operations or the online calculation of activation functions.
Fast approximate reciprocal approximations for iterative algorithms
For this reason, we present a low complexity non-iterative approximation of the reciprocal function.
