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Found 16 papers, 6 papers with code
Atom: Low-bit Quantization for Efficient and Accurate LLM Serving
To maximize LLMs' serving throughput, we introduce Atom, a low-bit quantization method that achieves high throughput improvements with negligible accuracy loss.
Punica: Multi-Tenant LoRA Serving
Our scheduler consolidates multi-tenant LoRA serving workloads in a shared GPU cluster.
SparseTIR: Composable Abstractions for Sparse Compilation in Deep Learning
We propose SparseTIR, a sparse tensor compilation abstraction that offers composable formats and composable transformations for deep learning workloads.
Characterizing and Taming Resolution in Convolutional Neural Networks
Image resolution has a significant effect on the accuracy and computational, storage, and bandwidth costs of computer vision model inference.
Cloud Collectives: Towards Cloud-aware Collectives forML Workloads with Rank Reordering
ML workloads are becoming increasingly popular in the cloud.
Accelerating SpMM Kernel with Cache-First Edge Sampling for Graph Neural Networks
To evaluate ES-SpMM's performance, we integrated it with a popular GNN framework, DGL, and tested it using representative GNN models and datasets.
Automated Backend-Aware Post-Training Quantization
Quantization is a key technique to reduce the resource requirement and improve the performance of neural network deployment.
Synthesizing Number Generators for Stochastic Computing using Mixed Integer Programming
Stochastic computing (SC) is a high density, low-power computation technique which encodes values as unary bitstreams instead of binary-encoded (BE) values.
Emerging Technologies
Automating Generation of Low Precision Deep Learning Operators
To date, none of the popular deep learning directly support low precision operators, partly due to a lack of optimized low precision libraries.
Stochastic Synthesis for Stochastic Computing
Stochastic computing (SC) is an emerging computing technique which offers higher computational density, and lower power over binary-encoded (BE) computation.
Emerging Technologies
A Hardware-Software Blueprint for Flexible Deep Learning Specialization
Specialized Deep Learning (DL) acceleration stacks, designed for a specific set of frameworks, model architectures, operators, and data types, offer the allure of high performance while sacrificing flexibility.
Parameter Hub: a Rack-Scale Parameter Server for Distributed Deep Neural Network Training
Distributed deep neural network (DDNN) training constitutes an increasingly important workload that frequently runs in the cloud.
Learning to Optimize Tensor Programs
Efficient implementations of tensor operators, such as matrix multiplication and high dimensional convolution, are key enablers of effective deep learning systems.
TVM: An Automated End-to-End Optimizing Compiler for Deep Learning
Experimental results show that TVM delivers performance across hardware back-ends that are competitive with state-of-the-art, hand-tuned libraries for low-power CPU, mobile GPU, and server-class GPUs.
Clustering Billions of Reads for DNA Data Storage
We provide empirical justification of the accuracy, scalability, and convergence of our algorithm on real and synthetic data.
MATIC: Learning Around Errors for Efficient Low-Voltage Neural Network Accelerators
As a result of the increasing demand for deep neural network (DNN)-based services, efforts to develop dedicated hardware accelerators for DNNs are growing rapidly.
