8 citations · 24 across the 10 of their papers we have counts for
11 papers
TerMeZO: Ternary Sparse Zeroth-Order Optimization for Fine-tuning BitNet Models at the Edge
Houssem Sifaou, Prabodh Katti, Bipin Rajendran +1
Fine-tuning anguage models (LLMs) with first-order optimizers requires a memory several times larger than that required for inference. Memory-efficient zeroth-order optimization (M…
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…
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…
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…
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…
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…
Baseline Drift Tolerant Signal Encoding for ECG Classification with Deep Learning
Robert O Shea, Prabodh Katti, Bipin Rajendran
Common artefacts such as baseline drift, rescaling, and noise critically limit the performance of machine learningbased automated ECG analysis and interpretation. This study propos…
Stochastic Spiking Attention: Accelerating Attention with Stochastic Computing in Spiking Networks
Zihang Song, Prabodh Katti, Osvaldo Simeone +1
Spiking Neural Networks (SNNs) have been recently integrated into Transformer architectures due to their potential to reduce computational demands and to improve power efficiency.…
Bayesian Inference Accelerator for Spiking Neural Networks
Prabodh Katti, Anagha Nimbekar, Chen Li +3
Bayesian neural networks offer better estimates of model uncertainty compared to frequentist networks. However, inference involving Bayesian models requires multiple instantiations…
Bayesian Inference on Binary Spiking Networks Leveraging Nanoscale Device Stochasticity
Prabodh Katti, Nicolas Skatchkovsky, Osvaldo Simeone +2
Bayesian Neural Networks (BNNs) can overcome the problem of overconfidence that plagues traditional frequentist deep neural networks, and are hence considered to be a key enabler f…