Publications by authors named "Paragkumar Chaudhari"
Article Synopsis
- Shape morphing is crucial for movement in tiny organisms, and the study introduces tiny robots (metabots) that can change their shape effectively.
- These robots utilize a kirigami design with parts ranging from 10 nm to 100 μm, allowing them to expand and contract rapidly, achieving movement within 100 ms.
- The technology allows for customizable shapes and motion patterns, paving the way for diverse applications like micromachines, adjustable optical devices, and small-scale medical tools.
Runtime Programmable and Memory Bandwidth Optimized FPGA-Based Coprocessor for Deep Convolutional Neural Network.
IEEE Trans Neural Netw Learn Syst
December 2018
The deep convolutional neural network (DCNN) is a class of machine learning algorithms based on feed-forward artificial neural network and is widely used for image processing applications. Implementation of DCNN in real-world problems needs high computational power and high memory bandwidth, in a power-constrained environment. A general purpose CPU cannot exploit different parallelisms offered by these algorithms and hence is slow and energy inefficient for practical use.
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