Skanda Koppula

TL;DR: EDEN is the first general framework that reduces DNN energy consumption and DNN evaluation latency by using approximate DRAM devices, while strictly meeting a user-specified target DNN accuracy, and reliably improves the error resiliency of the DNN by an order of magnitude.

TL;DR: In this article, the authors propose a hardware-software cooperative mechanism that enables highly efficient indexing and storage of sparse matrices by explicitly enabling the hardware to recognize and exploit sparsity in data.

TL;DR: This paper proposes SMASH, a hardware-software cooperative mechanism that enables highly-efficient indexing and storage of sparse matrices and devise a novel software encoding based on a hierarchy of bitmaps that can be used to efficiently compress any sparse matrix, regardless of the extent and structure of sparsity.

TL;DR: Odin is proposed, a self-supervised learning paradigm that discovers meaningful image segmentations without any supervision and achieves state-of-the-art transfer learning results for object detection and instance segmentation on COCO, and semantic segmentsation on PASCAL and Cityscapes, while strongly surpassing supervised pre-training for video segmentations on DAVIS.

TL;DR: EDEN as mentioned in this paper proposes a general framework that reduces DNN energy consumption and DNN evaluation latency by using approximate DRAM devices, while strictly meeting a user-specified target DNN accuracy.