Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Hardware calibrated learning to compensate heterogeneity in analog
  RRAM-based Spiking Neural Networks   Spiking Neural Networks (SNNs) can unleash the full power of analog Resistive
Random Access Memories (RRAMs) based circuits for low power signal processing.
Their inherent computational sparsity naturally results in energy efficiency
benefits. The main challenge implementing robust SNNs is the intrinsic
variability (heterogeneity) of both analog CMOS circuits and RRAM technology.
In this work, we assessed the performance and variability of RRAM-based
neuromorphic circuits that were designed and fabricated using a 130\,nm
technology node. Based on these results, we propose a Neuromorphic Hardware
Calibrated (NHC) SNN, where the learning circuits are calibrated on the
measured data. We show that by taking into account the measured heterogeneity
characteristics in the off-chip learning phase, the NHC SNN self-corrects its
hardware non-idealities and learns to solve benchmark tasks with high accuracy.
This work demonstrates how to cope with the heterogeneity of neurons and
synapses for increasing classification accuracy in temporal tasks.

9
The categories of this paper are: nlin
23
The categories of this paper are: solv-int
5
The categories of this paper are: gr-qc
20
The categories of this paper are: acc-phys
36
The categories of this paper are: cmp-lg
12
The categories of this paper are: nucl-th
10
The categories of this paper are: q-bio
17
The categories of this paper are: q-fin
32
The categories of this paper are: atom-ph
13
The categories of this paper are: quant-ph
31
The categories of this paper are: ao-sci
16
The categories of this paper are: stat
8
The categories of this paper are: hep-ex
11
The categories of this paper are: hep-lat
7
The categories of this paper are: hep-th
28
The categories of this paper are: alg-geom