Tuesday, December 5, 2023, @ 8:00-9:00am PT you are invited to attend an INRC Forum talk from Michael Jurado of GA Tech.
Title:
Spikemoid loss and the sparsification training.
Abstract:
The 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.
Spikemax 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.
Meeting link to join is available to INRC members and affiliates on the /wiki/spaces/forum/pages/1983578113.
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Bio: 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.
Recording:
For the recording and slides, see the full /wiki/spaces/forum/pages/1983578113 (accessible only to INRC Affiliates and Engaged Members).
If you are interested in becoming a member, here is the information about ”Joining the INRC.”