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Found 23 papers, 4 papers with code
LowRA: Accurate and Efficient LoRA Fine-Tuning of LLMs under 2 Bits
Fine-tuning large language models (LLMs) is increasingly costly as models scale to hundreds of billions of parameters, and even parameter-efficient fine-tuning (PEFT) methods like LoRA remain resource-intensive.
Adaptive Self-improvement LLM Agentic System for ML Library Development
In order to evaluate the effectiveness of our system, we construct a benchmark of a typical ML library and generate ASPL code with both open and closed-source LLMs on this benchmark.
SambaNova SN40L: Scaling the AI Memory Wall with Dataflow and Composition of Experts
In this paper, we describe how combining CoE, streaming dataflow, and a three-tier memory system scales the AI memory wall.
BaCO: A Fast and Portable Bayesian Compiler Optimization Framework
We introduce the Bayesian Compiler Optimization framework (BaCO), a general purpose autotuner for modern compilers targeting CPUs, GPUs, and FPGAs.
Efficient Memory Partitioning in Software Defined Hardware
As programmers turn to software-defined hardware (SDH) to maintain a high level of productivity while programming hardware to run complex algorithms, heavy-lifting must be done by the compiler to automatically partition on-chip arrays.
Prior-guided Bayesian Optimization
While Bayesian Optimization (BO) is a very popular method for optimizing expensive black-box functions, it fails to leverage the experience of domain experts.
Bayesian Optimization with a Prior for the Optimum
We show that BOPrO is around 6. 67x faster than state-of-the-art methods on a common suite of benchmarks, and achieves a new state-of-the-art performance on a real-world hardware design application.
Taurus: A Data Plane Architecture for Per-Packet ML
Emerging applications -- cloud computing, the internet of things, and augmented/virtual reality -- demand responsive, secure, and scalable datacenter networks.
Serving Recurrent Neural Networks Efficiently with a Spatial Accelerator
Recurrent Neural Network (RNN) applications form a major class of AI-powered, low-latency data center workloads.
Polystore++: Accelerated Polystore System for Heterogeneous Workloads
Modern real-time business analytic consist of heterogeneous workloads (e. g, database queries, graph processing, and machine learning).
DeepFreak: Learning Crystallography Diffraction Patterns with Automated Machine Learning
In this paper, we introduce a new serial crystallography dataset comprised of real and synthetic images; the synthetic images are generated through the use of a simulator that is both scalable and accurate.
MLSys: The New Frontier of Machine Learning Systems
Machine learning (ML) techniques are enjoying rapidly increasing adoption.
Practical Design Space Exploration
The proposed methodology follows a white-box model which is simple to understand and interpret (unlike, for example, neural networks) and can be used by the user to better understand the results of the automatic search.
DiffraNet: Automatic Classification of Serial Crystallography Diffraction Patterns
In this paper, we introduce a new serial crystallography dataset generated through the use of a simulator; the synthetic images are labeled and they are both scalable and accurate.
Analysis of DAWNBench, a Time-to-Accuracy Machine Learning Performance Benchmark
In this work, we analyze the entries from DAWNBench, which received optimized submissions from multiple industrial groups, to investigate the behavior of TTA as a metric as well as trends in the best-performing entries.
High-Accuracy Low-Precision Training
Low-precision computation is often used to lower the time and energy cost of machine learning, and recently hardware accelerators have been developed to support it.
Infrastructure for Usable Machine Learning: The Stanford DAWN Project
Despite incredible recent advances in machine learning, building machine learning applications remains prohibitively time-consuming and expensive for all but the best-trained, best-funded engineering organizations.
Ensuring Rapid Mixing and Low Bias for Asynchronous Gibbs Sampling
Gibbs sampling is a Markov chain Monte Carlo technique commonly used for estimating marginal distributions.
Taming the Wild: A Unified Analysis of Hogwild-Style Algorithms
Stochastic gradient descent (SGD) is a ubiquitous algorithm for a variety of machine learning problems.
Rapidly Mixing Gibbs Sampling for a Class of Factor Graphs Using Hierarchy Width
Gibbs sampling on factor graphs is a widely used inference technique, which often produces good empirical results.
Taming the Wild: A Unified Analysis of Hogwild!-Style Algorithms
with relaxed assumptions on the sparsity of the problem; (2) we analyze asynchronous SGD algorithms for non-convex matrix problems including matrix completion; and (3) we design and analyze an asynchronous SGD algorithm, called Buckwild!, that uses lower-precision arithmetic.
Global Convergence of Stochastic Gradient Descent for Some Non-convex Matrix Problems
Stochastic gradient descent (SGD) on a low-rank factorization is commonly employed to speed up matrix problems including matrix completion, subspace tracking, and SDP relaxation.
Utilizing Static Analysis and Code Generation to Accelerate Neural Networks
In this paper, we present SONNC, a compiler for NNs that utilizes static analysis to generate optimized parallel code.
