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Found 8 papers, 0 papers with code
Non-Ideal Program-Time Conservation in Charge Trap Flash for Deep Learning
Training deep neural networks (DNNs) is computationally intensive but arrays of non-volatile memories like Charge Trap Flash (CTF) can accelerate DNN operations using in-memory computing.
FeFET-based MirrorBit cell for High-density NVM storage
A TCAD simulation is also presented to explain the origin and working of MirrorBit states based on the FeFET model calibrated using the GlobalFoundries FeFET device.
A temporally and spatially local spike-based backpropagation algorithm to enable training in hardware
Although signed gradient values are a challenge for spike-based representation, we tackle this by splitting the gradient signal into positive and negative streams.
Algorithm For 3D-Chemotaxis Using Spiking Neural Network
In this work, we aim to devise an end-to-end spiking implementation for contour tracking in 3D media inspired by chemotaxis, where the worm reaches the region which has the given set concentration.
Simplified Klinokinesis using Spiking Neural Networks for Resource-Constrained Navigation on the Neuromorphic Processor Loihi
C. elegans shows chemotaxis using klinokinesis where the worm senses the concentration based on a single concentration sensor to compute the concentration gradient to perform foraging through gradient ascent/descent towards the target concentration followed by contour tracking.
Hardware-Friendly Synaptic Orders and Timescales in Liquid State Machines for Speech Classification
In this work, we analyze the role of synaptic orders namely: {\delta} (high output for single time step), 0th (rectangular with a finite pulse width), 1st (exponential fall) and 2nd order (exponential rise and fall) and synaptic timescales on the reservoir output response and on the TI-46 spoken digits classification accuracy under a more comprehensive parameter sweep.
Adaptive Chemotaxis for improved Contour Tracking using Spiking Neural Networks
In particular, we focus on the problem of contour tracking, wherein the bot must reach and subsequently follow a desired concentration setpoint.
Predicting Performance using Approximate State Space Model for Liquid State Machines
Lyapunov exponent (mu), used to characterize the "non-linearity" of the network, correlates well with LSM performance.
