18 citations · 74 across the 23 of their papers we have counts for
6 papers · 1 filter
Estimation of Energy-dissipation Lower-bounds for Neuromorphic Learning-in-memory
Zihao Chen, Faiek Ahsan, Johannes Leugering +2
Neuromorphic or neurally-inspired optimizers rely on local but parallel parameter updates to solve problems that range from quadratic programming to Ising machines. An ideal realiz…
Multiplierless In-filter Computing for tinyML Platforms
Abhishek Ramdas Nair, Pallab Kumar Nath, Shantanu Chakrabartty +1
Wildlife conservation using continuous monitoring of environmental factors and biomedical classification, which generate a vast amount of sensor data, is a challenge due to limited…
A Framework for Analyzing Cross-correlators using Price's Theorem and Piecewise-Linear Decomposition
Zhili Xiao, Shantanu Chakrabartty
Precise estimation of cross-correlation or similarity between two random variables lies at the heart of signal detection, hyperdimensional computing, associative memories, and neur…
Multiplierless MP-Kernel Machine For Energy-efficient Edge Devices
Abhishek Ramdas Nair, Pallab Kumar Nath, Shantanu Chakrabartty +1
We present a novel framework for designing multiplierless kernel machines that can be used on resource-constrained platforms like intelligent edge devices. The framework uses a pie…
Multiplierless and Sparse Machine Learning based on Margin Propagation Networks
Nazreen P. M., Shantanu Chakrabartty, Chetan Singh Thakur
The new generation of machine learning processors have evolved from multi-core and parallel architectures that were designed to efficiently implement matrix-vector-multiplications…
Resonant Machine Learning Based on Complex Growth Transform Dynamical Systems
Oindrila Chatterjee, Shantanu Chakrabartty
Traditional energy-based learning models associate a single energy metric to each configuration of variables involved in the underlying optimization process. Such models associate…