arXiv subjects

Ashutosh Pattnaik

Publications and source records attributed to Ashutosh Pattnaik.

2 recordsLinked to original sources

arXiv2026

A study of the kinematic and volumetric co-evolution of Earth-directed CMEs

While flare-associated CMEs generally show a strong association between flare X-ray flux and CME kinematics, their volumetric evolution and its link to both kinematics and flare activity remains less explored. In this study, we investigate the volumetric and kinematic co-evolution of ten Earth-directed, flare-associated CMEs using multi-viewpoint observations from STEREO-A, STEREO-B, and SOHO. We perform 3D reconstructions of the CME flux ropes with the Graduated Cylindrical Shell (GCS) model and derive their geometrical parameters. We find that the total CME volume follows a power-law dependence on the leading edge height, and that different structural components expand at different rates, with the ellipsoidal front expanding faster than the conical legs. Furthermore, the volumetric evolution follows a multi-phase pattern: initial overexpansion, a gradual reduction in the expansion rate, and finally saturation at a higher heliocentric distance. This is similar to the well-established three-phase evolution of the CME kinematics. Notably, the second-order derivative of volume with time shows a strong temporal correlation with both CME acceleration and the GOES soft X-ray flux of the associated flare. This is the first study to report such a correspondence between volumetric evolution and flare timing, highlighting the role of flare energy release in governing CME expansion dynamics. Our findings motivate further studies into the coupling between magnetic reconnection and CME volumetric evolution in the corona.

astro-ph.SR↗

arXiv2021

Exploiting Activation based Gradient Output Sparsity to Accelerate Backpropagation in CNNs

Machine/deep-learning (ML/DL) based techniques are emerging as a driving force behind many cutting-edge technologies, achieving high accuracy on computer vision workloads such as image classification and object detection. However, training these models involving large parameters is both time-consuming and energy-hogging. In this regard, several prior works have advocated for sparsity to speed up the of DL training and more so, the inference phase. This work begins with the observation that during training, sparsity in the forward and backward passes are correlated. In that context, we investigate two types of sparsity (input and output type) inherent in gradient descent-based optimization algorithms and propose a hardware micro-architecture to leverage the same. Our experimental results use five state-of-the-art CNN models on the Imagenet dataset, and show back propagation speedups in the range of 1.69$\times$ to 5.43$\times$, compared to the dense baseline execution. By exploiting sparsity in both the forward and backward passes, speedup improvements range from 1.68$\times$ to 3.30$\times$ over the sparsity-agnostic baseline execution. Our work also achieves significant reduction in training iteration time over several previously proposed dense as well as sparse accelerator based platforms, in addition to achieving order of magnitude energy efficiency improvements over GPU based execution.

cs.LG↗