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Found 7 papers, 1 papers with code
Generative AI Beyond LLMs: System Implications of Multi-Modal Generation
As the development of large-scale Generative AI models evolve beyond text (1D) generation to include image (2D) and video (3D) generation, processing spatial and temporal information presents unique challenges to quality, performance, and efficiency.
MAD Max Beyond Single-Node: Enabling Large Machine Learning Model Acceleration on Distributed Systems
Training and deploying large machine learning (ML) models is time-consuming and requires significant distributed computing infrastructures.
MP-Rec: Hardware-Software Co-Design to Enable Multi-Path Recommendation
Based on our characterization of various embedding representations, we propose a hybrid embedding representation that achieves higher quality embeddings at the cost of increased memory and compute requirements.
RecPipe: Co-designing Models and Hardware to Jointly Optimize Recommendation Quality and Performance
Thus, we design RecPipeAccel (RPAccel), a custom accelerator that jointly optimizes quality, tail-latency, and system throughput.
RecSSD: Near Data Processing for Solid State Drive Based Recommendation Inference
Neural personalized recommendation models are used across a wide variety of datacenter applications including search, social media, and entertainment.
Cross-Stack Workload Characterization of Deep Recommendation Systems
Deep learning based recommendation systems form the backbone of most personalized cloud services.
DeepRecSys: A System for Optimizing End-To-End At-scale Neural Recommendation Inference
Neural personalized recommendation is the corner-stone of a wide collection of cloud services and products, constituting significant compute demand of the cloud infrastructure.
Distributed, Parallel, and Cluster Computing
