Search Results for author:
Found 27 papers, 11 papers with code
BioSimulators: a central registry of simulation engines and services for recommending specific tools
To help investigators find and use simulation tools, we developed BioSimulators (https://biosimulators. org), a central registry of the capabilities of simulation tools and consistent Python, command-line, and containerized interfaces to each version of each tool.
Tabula: Efficiently Computing Nonlinear Activation Functions for Secure Neural Network Inference
Multiparty computation approaches to secure neural network inference traditionally rely on garbled circuits for securely executing nonlinear activation functions.
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.
Tabula: Efficiently Computing Nonlinear Activation Functions for Private Neural Network Inference
Multiparty computation approaches to private neural network inference require significant communication between server and client, incur tremendous runtime penalties, and cost massive storage overheads.
Gradient Disaggregation: Breaking Privacy in Federated Learning by Reconstructing the User Participant Matrix
We show that aggregated model updates in federated learning may be insecure.
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.
AutoPilot: Automating SoC Design Space Exploration for SWaP Constrained Autonomous UAVs
Balancing a computing system for a UAV requires considering both the cyber (e. g., sensor rate, compute performance) and physical (e. g., payload weight) characteristics that affect overall performance.
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.
Performance of the Dark Energy Spectroscopic Instrument(DESI) Fiber System
The recently commissioned Dark Energy Spectroscopic Instrument (DESI) will measure the expansion historyof the universe using the Baryon Acoustic Oscillation technique.
Instrumentation and Methods for Astrophysics
EdgeBERT: Sentence-Level Energy Optimizations for Latency-Aware Multi-Task NLP Inference
Transformer-based language models such as BERT provide significant accuracy improvement for a multitude of natural language processing (NLP) tasks.
Cross-Stack Workload Characterization of Deep Recommendation Systems
Deep learning based recommendation systems form the backbone of most personalized cloud services.
Candidate Periodically Variable Quasars from the Dark Energy Survey and the Sloan Digital Sky Survey
We present a systematic search for periodic light curves in 625 spectroscopically confirmed quasars with a median redshift of 1. 8 in a 4. 6 deg$^2$ overlapping region of the Dark Energy Survey Supernova (DES-SN) fields and the Sloan Digital Sky Survey Stripe 82 (SDSS-S82).
High Energy Astrophysical Phenomena Astrophysics of Galaxies
Dark Energy Survey Year 1 Results: Cosmological Constraints from Cluster Abundances and Weak Lensing
We perform a joint analysis of the counts and weak lensing signal of redMaPPer clusters selected from the Dark Energy Survey (DES) Year 1 dataset.
Cosmology and Nongalactic Astrophysics
CHIPKIT: An agile, reusable open-source framework for rapid test chip development
The current trend for domain-specific architectures (DSAs) has led to renewed interest in research test chips to demonstrate new specialized hardware.
Hardware Architecture
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
SMAUG: End-to-End Full-Stack Simulation Infrastructure for Deep Learning Workloads
In recent years, there has been tremendous advances in hardware acceleration of deep neural networks.
A binary-activation, multi-level weight RNN and training algorithm for ADC-/DAC-free and noise-resilient processing-in-memory inference with eNVM
We propose a new algorithm for training neural networks with binary activations and multi-level weights, which enables efficient processing-in-memory circuits with embedded nonvolatile memories (eNVM).
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.
AdaptivFloat: A Floating-point based Data Type for Resilient Deep Learning Inference
Conventional hardware-friendly quantization methods, such as fixed-point or integer, tend to perform poorly at very low word sizes as their shrinking dynamic ranges cannot adequately capture the wide data distributions commonly seen in sequence transduction models.
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.
Exploiting Parallelism Opportunities with Deep Learning Frameworks
State-of-the-art machine learning frameworks support a wide variety of design features to enable a flexible machine learning programming interface and to ease the programmability burden on machine learning developers.
Benchmarking TPU, GPU, and CPU Platforms for Deep Learning
Training deep learning models is compute-intensive and there is an industry-wide trend towards hardware specialization to improve performance.
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.
The Buzzard Flock: Dark Energy Survey Synthetic Sky Catalogs
We show that the weak-lensing shear catalog, redMaGiC galaxy catalogs and redMaPPer cluster catalogs provide plausible realizations of the same catalogs in the DES Y1 data by comparing their magnitude, color and redshift distributions, angular clustering, and mass-observable relations, making them useful for testing analyses that use these samples.
Cosmology and Nongalactic Astrophysics
Cloud No Longer a Silver Bullet, Edge to the Rescue
This paper takes the position that, while cognitive computing today relies heavily on the cloud, we will soon see a paradigm shift where cognitive computing primarily happens on network edges.
Weightless: Lossy Weight Encoding For Deep Neural Network Compression
This results in up to a 1. 51x improvement over the state-of-the-art.
Fathom: Reference Workloads for Modern Deep Learning Methods
Fathom has been released online, and this paper focuses on understanding the fundamental performance characteristics of each model.
