6 papers
Event-triggered Implicit Perturbation for Zeroth-Order Fine-Tuning of Spiking Transformers
Tengteng Lei, Prabodh Katti, Rashi Dutt +5
Zeroth-order (ZO) optimization estimates gradients using only forward-pass evaluations, making it suitable for fine-tuning non-differentiable, event-driven spiking neural networks…
On-Device Fine-Tuning via Backprop-Free Zeroth-Order Optimization
Prabodh Katti, Houssem Sifaou, Sangwoo Park +2
On-device fine-tuning is a critical capability for edge AI systems, which must support adaptation to different agentic tasks under stringent memory constraints. Conventional backpr…
Xpikeformer: Hybrid Analog-Digital Hardware Acceleration for Spiking Transformers
Zihang Song, Prabodh Katti, Osvaldo Simeone +1
The integration of neuromorphic computing and transformers through spiking neural networks (SNNs) offers a promising path to energy-efficient sequence modeling, with the potential…
Bayes2IMC: In-Memory Computing for Bayesian Binary Neural Networks
Prabodh Katti, Clement Ruah, Osvaldo Simeone +2
Bayesian Neural Networks (BNNs) provide superior estimates of uncertainty by generating an ensemble of predictive distributions. However, inference via ensembling is resource-inten…
Sparsity-Aware Optimization of In-Memory Bayesian Binary Neural Network Accelerators
Prabodh Katti, Bashir M. Al-Hashimi, Bipin Rajendran
Bayesian Neural Networks (BNNs) provide principled estimates of model and data uncertainty by encoding parameters as distributions. This makes them key enablers for reliable AI tha…
Hardware-Software Co-optimised Fast and Accurate Deep Reconfigurable Spiking Inference Accelerator Architecture Design Methodology
Anagha Nimbekar, Prabodh Katti, Chen Li +3
Spiking Neural Networks (SNNs) have emerged as a promising approach to improve the energy efficiency of machine learning models, as they naturally implement event-driven computatio…