3 citations · 3 across the 5 of their papers we have counts for
5 papers
QCQA: Quality and Capacity-aware grouped Query Attention
Vinay Joshi, Prashant Laddha, Shambhavi Sinha +2
Excessive memory requirements of key and value features (KV-cache) present significant challenges in the autoregressive inference of large language models (LLMs), restricting both…
CiMNet: Towards Joint Optimization for DNN Architecture and Configuration for Compute-In-Memory Hardware
Souvik Kundu, Anthony Sarah, Vinay Joshi +2
With the recent growth in demand for large-scale deep neural networks, compute in-memory (CiM) has come up as a prominent solution to alleviate bandwidth and on-chip interconnect b…
Robust 3D Scene Segmentation through Hierarchical and Learnable Part-Fusion
Anirud Thyagharajan, Benjamin Ummenhofer, Prashant Laddha +2
3D semantic segmentation is a fundamental building block for several scene understanding applications such as autonomous driving, robotics and AR/VR. Several state-of-the-art seman…
Proximu$: Efficiently Scaling DNN Inference in Multi-core CPUs through Near-Cache Compute
Anant V. Nori, Rahul Bera, Shankar Balachandran +5
Deep Neural Network (DNN) inference is emerging as the fundamental bedrock for a multitude of utilities and services. CPUs continue to scale up their raw compute capabilities for D…
AccSS3D: Accelerator for Spatially Sparse 3D DNNs
Om Ji Omer, Prashant Laddha, Gurpreet S Kalsi +6
Semantic understanding and completion of real world scenes is a foundational primitive of 3D Visual perception widely used in high-level applications such as robotics, medical imag…