Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: FSpiNN: An Optimization Framework for Memory- and Energy-Efficient
  Spiking Neural Networks   Spiking Neural Networks (SNNs) are gaining interest due to their event-driven
processing which potentially consumes low power/energy computations in hardware
platforms, while offering unsupervised learning capability due to the
spike-timing-dependent plasticity (STDP) rule. However, state-of-the-art SNNs
require a large memory footprint to achieve high accuracy, thereby making them
difficult to be deployed on embedded systems, for instance on battery-powered
mobile devices and IoT Edge nodes. Towards this, we propose FSpiNN, an
optimization framework for obtaining memory- and energy-efficient SNNs for
training and inference processing, with unsupervised learning capability while
maintaining accuracy. It is achieved by (1) reducing the computational
requirements of neuronal and STDP operations, (2) improving the accuracy of
STDP-based learning, (3) compressing the SNN through a fixed-point
quantization, and (4) incorporating the memory and energy requirements in the
optimization process. FSpiNN reduces the computational requirements by reducing
the number of neuronal operations, the STDP-based synaptic weight updates, and
the STDP complexity. To improve the accuracy of learning, FSpiNN employs
timestep-based synaptic weight updates, and adaptively determines the STDP
potentiation factor and the effective inhibition strength. The experimental
results show that, as compared to the state-of-the-art work, FSpiNN achieves
7.5x memory saving, and improves the energy-efficiency by 3.5x on average for
training and by 1.8x on average for inference, across MNIST and Fashion MNIST
datasets, with no accuracy loss for a network with 4900 excitatory neurons,
thereby enabling energy-efficient SNNs for edge devices/embedded systems.

17
The categories of this paper are: q-fin
2
The categories of this paper are: math
29
The categories of this paper are: funct-an
10
The categories of this paper are: q-bio
26
The categories of this paper are: bayes-an
22
The categories of this paper are: chao-dyn
3
The categories of this paper are: physics
33
The categories of this paper are: plasm-ph
28
The categories of this paper are: alg-geom
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The categories of this paper are: solv-int
13
The categories of this paper are: quant-ph
34
The categories of this paper are: chem-ph
18
The categories of this paper are: econ
6
The categories of this paper are: astro-ph
37
The categories of this paper are: supr-con
30
The categories of this paper are: q-alg