7 citations · 7 across the 7 of their papers we have counts for
7 papers
FOLIO: Focused Semantic Memory for Streaming Video Understanding
Haoyang Fan, Dhruv Parikh, Anvitha Ramachandran +4
In online streaming video understanding, a video stream continues to arrive and queries may be issued at any time. Because streaming frames grow without bound, the system must cont…
LLM-Guided ANN Index Optimization for Human-Object Interaction Retrieval
Shahrzad Esmat, Chaunte W. Lacewell, Sameh Gobriel +2
Retrieval systems underpin modern AI applications -- spanning visual search, recommendation engines, and multi-modal question answering. Modern multi-stage retrieval systems requir…
KVCrush: Key value cache size-reduction using similarity in head-behaviour
Gopi Krishna Jha, Sameh Gobriel, Liubov Talamanova +1
Key-value (KV) caching has emerged as a crucial optimization technique for accelerating inference in large language models (LLMs). By allowing the attention operation to scale line…
SparAMX: Accelerating Compressed LLMs Token Generation on AMX-powered CPUs
Ahmed F. AbouElhamayed, Jordan Dotzel, Yash Akhauri +6
Large language models have high compute, latency, and memory requirements. While specialized accelerators such as GPUs and TPUs typically run these workloads, CPUs are more widely…
ARGO: An Auto-Tuning Runtime System for Scalable GNN Training on Multi-Core Processor
Yi-Chien Lin, Yuyang Chen, Sameh Gobriel +3
As Graph Neural Networks (GNNs) become popular, libraries like PyTorch-Geometric (PyG) and Deep Graph Library (DGL) are proposed; these libraries have emerged as the de facto stand…
Mem-Rec: Memory Efficient Recommendation System using Alternative Representation
Gopi Krishna Jha, Anthony Thomas, Nilesh Jain +3
Deep learning-based recommendation systems (e.g., DLRMs) are widely used AI models to provide high-quality personalized recommendations. Training data used for modern recommendatio…
Accelerating SLIDE Deep Learning on Modern CPUs: Vectorization, Quantizations, Memory Optimizations, and More
Shabnam Daghaghi, Nicholas Meisburger, Mengnan Zhao +4
Deep learning implementations on CPUs (Central Processing Units) are gaining more traction. Enhanced AI capabilities on commodity x86 architectures are commercially appealing due t…