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DTSTAMP:20260202T201248Z
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DTSTART;TZID=America/Chicago:20251120T080000
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UID:submissions.supercomputing.org_SC25_sess533_post249@linklings.com
SUMMARY:IncineRate: Multi-Modal FPGA Accelerator for SCNNs
DESCRIPTION:Björn A. Lindqvist and Artur Podobas (KTH Royal Institute of T
 echnology)\n\nSpiking neural networks (SNNs) are a promising alternative t
 o conventional artificial neural networks (ANNs) due to their biological i
 nterpretability and capability to exploit sparse computation. Specialized 
 hardware for SNNs has advantages over general-purpose devices in terms of 
 power and performance. However, the computational requirements of modern s
 piking convolutional neural networks (SCNNs) render most SNN hardware inef
 ficient for SCNN acceleration. Therefore, we present IncineRate, a flexibl
 e FPGA-based SCNN accelerator architecture. IncineRate has built-in suppor
 t for many SCNNs, such as AlexNet, VGG16, and ResNets, and can be extended
  to support other network models. The number of simulation time steps, the
  network architecture, and other settings are specified at run time, allow
 ing an already deployed device to execute multiple networks without reconf
 iguration. Our results show that IncineRate achieves state-of-the-art clas
 sification accuracy among FPGA-based SCNNs on CIFAR10 and CIFAR100.\n\nTag
 : Research & ACM SRC Posters\n\nRegistration Category: Technical Program R
 eg Pass\n\nSession Chairs: Kento Sato (RIKEN Center for Computational Scie
 nce (R-CCS)); Chris Schlipalius (Pawsey Supercomputing Research Centre; Co
 mmonwealth Scientific and Industrial Research Organisation (CSIRO), Austra
 lia); and Anja Gerbes (Georg-August-Universität Göttingen)\n\n
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