15,124 research outputs found
Inherent Weight Normalization in Stochastic Neural Networks
Multiplicative stochasticity such as Dropout improves the robustness and
generalizability of deep neural networks. Here, we further demonstrate that
always-on multiplicative stochasticity combined with simple threshold neurons
are sufficient operations for deep neural networks. We call such models Neural
Sampling Machines (NSM). We find that the probability of activation of the NSM
exhibits a self-normalizing property that mirrors Weight Normalization, a
previously studied mechanism that fulfills many of the features of Batch
Normalization in an online fashion. The normalization of activities during
training speeds up convergence by preventing internal covariate shift caused by
changes in the input distribution. The always-on stochasticity of the NSM
confers the following advantages: the network is identical in the inference and
learning phases, making the NSM suitable for online learning, it can exploit
stochasticity inherent to a physical substrate such as analog non-volatile
memories for in-memory computing, and it is suitable for Monte Carlo sampling,
while requiring almost exclusively addition and comparison operations. We
demonstrate NSMs on standard classification benchmarks (MNIST and CIFAR) and
event-based classification benchmarks (N-MNIST and DVS Gestures). Our results
show that NSMs perform comparably or better than conventional artificial neural
networks with the same architecture
Neural Sampling Machine with Stochastic Synapse allows Brain-like Learning and Inference
Many real-world mission-critical applications require continual online
learning from noisy data and real-time decision making with a defined
confidence level. Probabilistic models and stochastic neural networks can
explicitly handle uncertainty in data and allow adaptive learning-on-the-fly,
but their implementation in a low-power substrate remains a challenge. Here, we
introduce a novel hardware fabric that implements a new class of stochastic NN
called Neural-Sampling-Machine that exploits stochasticity in synaptic
connections for approximate Bayesian inference. Harnessing the inherent
non-linearities and stochasticity occurring at the atomic level in emerging
materials and devices allows us to capture the synaptic stochasticity occurring
at the molecular level in biological synapses. We experimentally demonstrate
in-silico hybrid stochastic synapse by pairing a ferroelectric field-effect
transistor -based analog weight cell with a two-terminal stochastic selector
element. Such a stochastic synapse can be integrated within the
well-established crossbar array architecture for compute-in-memory. We
experimentally show that the inherent stochastic switching of the selector
element between the insulator and metallic state introduces a multiplicative
stochastic noise within the synapses of NSM that samples the conductance states
of the FeFET, both during learning and inference. We perform network-level
simulations to highlight the salient automatic weight normalization feature
introduced by the stochastic synapses of the NSM that paves the way for
continual online learning without any offline Batch Normalization. We also
showcase the Bayesian inferencing capability introduced by the stochastic
synapse during inference mode, thus accounting for uncertainty in data. We
report 98.25%accuracy on standard image classification task as well as
estimation of data uncertainty in rotated samples
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