Author

Sankeerth Durvasula

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#machine learning Preprint Oct 2026

Activation Sparsity with Weight Approximation for Faster LLM Decoding on Offloaded Weights

Deploying LLMs on consumer-grade GPUs with insufficient memory to hold their weights can result in prohibitively slow inference, because decoding repeatedly transfers offloaded weights from system RAM or flash storage into GPU at much lower bandwidth than local GPU-memory access. Activation sparsity reduces these trans...

Junehyung Kim, Sankeerth Durvasula, N. Vijaykumar · 0 citations

#machine learning Preprint Jun 2025

Squeeze3D: Extreme Neural Compression with Latent Space Bridging

Squeeze3D is a novel framework that leverages implicit prior knowledge learnt by existing pre-trained encoders and decoders to compress 3D data at extremely high compression ratios and can flexibly support different formats, including meshes, point clouds, and radiance fields.

Rishit Dagli, Yu-Shi Guan, Sankeerth Durvasula et al. · 0 citations

#machine learning Nov 2025

DCC: Data-Centric Compilation of Machine Learning Kernels for Processing-In-Memory Architectures

DCC is the first data-centric ML compiler for PIM systems that jointly co-optimizes data rearrangements and compute code in a unified tuning process to enable high performance execution.

Pei-Ming Yang, Sankeerth Durvasula, Ivan Fernandez et al. · International Symposium on C... · 1 citation