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DTSTART;VALUE=DATE:20231205
DTEND;VALUE=DATE:20231207
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SUMMARY:IEEE ICRC 2023
DESCRIPTION:The IEEE International Conference on Rebooting Computing is the premier venue for novel computing approaches\, including algorithms and languages\, system software\, system and network architectures\, new devices and circuits\, and applications of new materials and physics. This is an interdisciplinary conference that has participation from a broad technical community\, with emphasis on all aspects of the computing stack. \nIEEE ICRC 2023 is an in-person event with an option for virtual attendance. While all speakers will deliver their talks in-person\, attendees will have the option of attending the conference virtually. Check that option when you REGISTER! \nThe International Roadmap on Devices and Systems (IRDS) will also be featured at ICRC 2023 with talks from academia\, industry\, and government research centers spanning materials\, devices\, circuits\, and systems for computing.
URL:https://www.neuropac.info/event/ieee-icrc-2023/
LOCATION:San Diego\, San Diego\, CA\, United States
CATEGORIES:Conference
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DTSTART;TZID=America/Los_Angeles:20231205T080000
DTEND;TZID=America/Los_Angeles:20231205T090000
DTSTAMP:20260430T231423
CREATED:20231130T122725Z
LAST-MODIFIED:20231130T122725Z
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SUMMARY:Michael Jurado @ INRC - Enhancing Performance and Efficiency of SNNs
DESCRIPTION:Title:\nEnhancing Performance and Efficiency of SNNs: From Spike-Based Loss Improvements to Synaptic Sparsification Techniques. \nAbstract:\nThe introduction of offline training capabilities like Spike Layer Error Reassignment in Time (SLAYER) and advancements in the probabilistic interpretations of Spiking Neural Network (SNN) output reinforce SNNs as a viable alternative to Artificial Neural Networks (ANNs). However\, special care must be taken during Surrogate Gradient (SG) training to achieve desired performance and efficiency. This talk will cover our recent work in improving spike-based loss functions for SNNs as well as sparsifying SNNs for low cost\, high performant neuromorphic computing. \nSpikemax was previously introduced as a family of differentiable loss methods which use windowed spike counts to form classification probabilities. We modify the Spikemaxs loss method to use rates and a scaling parameter instead of counts to form Scaled-Spikemax. Our mathematical analysis shows that an appropriate scaling term can yield less coarse probability outputs from the SNN and help smooth the gradient of the loss during training. Experimentally\, we show that Scaled-Spikemax achieves faster training convergence than Spikemax and results in relative improvements of 4.2% and 9.9% in accuracy for NMNIST and N-TIDIGITS18\, respectively. We then extend Scaled-Spikemax to construct a spike-based loss function for multi-label classification called Spikemoid. The viability of Spikemoid is shown via the first known multi-label classification results on N-TIDIGITS18 and 2NMNIST\, a novel variation of NMNIST that superimposes event-driven sensory data. \nHowever\, SNNs trained through SG methods oftentimes use dense or convolutional connections which are not always suitable for Loihi2. In order to minimize core usage and power consumption on chip\, we employ synaptic pruning techniques as part of our SNN training pipelines. We demonstrate the effectiveness of synaptic pruning techniques for ANN to SNN conversion of vgg16 on Loihi1 as well as for a lava-dl trained SNN for the Intel DNS Challenge. This later approach involved the use of Gradual Magnitude Pruning (GMP) applied during SLAYER training\, which reduced the memory footprint of the baseline SDNN by 50-75%. We highlight infrastructure changes to netX which enable conversion of lava-dl trained SNNs into sparsity aware lava processes. \nMeeting link to join is available to INRC members and affiliates on the INRC Forum Schedule (click here). \nIf you are not yet a member of the INRC\, please see the “Joining the INRC link” below. \nBio: Michael Jurado is a research engineer at the Georgia Tech Research Institute. He studied computer science at Georgia Tech and received his master’s degree in Machine Learning in 2022. Lately\, Michael has been studying and developing neuromorphic algorithms for edge computing and a regular contributor to the lava code base. In his free time\, he likes to read and study languages. \n\n\n\n\n\n\n\n\nFor the recording and slides\, see the full INRC Forum 2023 Schedule (accessible only to INRC Affiliates and Engaged Members). \nIf you are interested in becoming a member\, here is the information about ”Joining the INRC.
URL:https://www.neuropac.info/event/michael-jurado-inrc-enhancing-performance-and-efficiency-of-snns/
LOCATION:Online
CATEGORIES:Talk
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BEGIN:VEVENT
DTSTART;VALUE=DATE:20231210
DTEND;VALUE=DATE:20231217
DTSTAMP:20260430T231423
CREATED:20231103T143805Z
LAST-MODIFIED:20231103T143805Z
UID:10000253-1702166400-1702771199@www.neuropac.info
SUMMARY:NeurIPS 2023
DESCRIPTION:Conference on Neural Information Processing Systems (NeurIPS) 2023\nNeurIPS 2023 will be held again at the at the New Orleans Ernest N. Morial Convention Center.\n\n\n\nThe conference was founded in 1987 and is now a multi-track interdisciplinary annual meeting that includes invited talks\, demonstrations\, symposia\, and oral and poster presentations of refereed papers. Along with the conference is a professional exposition focusing on machine learning in practice\, a series of tutorials\, and topical workshops that provide a less formal setting for the exchange of ideas.
URL:https://www.neuropac.info/event/neurips-2023/
LOCATION:New Orleans\, New Orleans\, LA\, United States
CATEGORIES:Conference
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