Search Results for author:
Found 16 papers, 7 papers with code
Carbon Connect: An Ecosystem for Sustainable Computing
For embodied carbon, we must re-think conventional design strategies -- over-provisioned monolithic servers, frequent hardware refresh cycles, custom silicon -- and adopt life-cycle design strategies that more effectively reduce, reuse and recycle hardware at scale.
Photonics for Sustainable Computing
Our analysis shows that photonics can reduce both operational and embodied carbon footprints with its high energy efficiency and at least 4$\times$ less fabrication carbon cost per unit area than 28 nm CMOS.
GPT-InvestAR: Enhancing Stock Investment Strategies through Annual Report Analysis with Large Language Models
This paper aims to simplify the process of assessing Annual Reports of all the firms by leveraging the capabilities of Large Language Models (LLMs).
Information Flow Control in Machine Learning through Modular Model Architecture
This lack of control for information flow from training data to model output is a major obstacle in training models on sensitive data when access control only allows individual users to access a subset of data.
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.
GPU-based Private Information Retrieval for On-Device Machine Learning Inference
Together, for various on-device ML applications such as recommendation and language modeling, our system on a single V100 GPU can serve up to $100, 000$ queries per second -- a $>100 \times$ throughput improvement over a CPU-based baseline -- while maintaining model accuracy.
Sustainable AI: Environmental Implications, Challenges and Opportunities
This paper explores the environmental impact of the super-linear growth trends for AI from a holistic perspective, spanning Data, Algorithms, and System Hardware.
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
MLPerf Training Benchmark
Machine learning (ML) needs industry-standard performance benchmarks to support design and competitive evaluation of the many emerging software and hardware solutions for ML.
MASR: A Modular Accelerator for Sparse RNNs
The architecture is enhanced by a series of dynamic activation optimizations that enable compact storage, ensure no energy is wasted computing null operations, and maintain high MAC utilization for highly parallel accelerator designs.
The Architectural Implications of Facebook's DNN-based Personalized Recommendation
The widespread application of deep learning has changed the landscape of computation in the data center.
Deep Learning Recommendation Model for Personalization and Recommendation Systems
With the advent of deep learning, neural network-based recommendation models have emerged as an important tool for tackling personalization and recommendation tasks.
Weightless: Lossy Weight Encoding For Deep Neural Network Compression
This results in up to a 1. 51x improvement over the state-of-the-art.
