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06/13/2023
INT2.1: Towards Fine-Tunable Quantized Large Language Models with Error Correction through Low-Rank Adaptation
We introduce a method that dramatically reduces fine-tuning VRAM require...
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06/09/2023
S^3: Increasing GPU Utilization during Generative Inference for Higher Throughput
Generating texts with a large language model (LLM) consumes massive amou...
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05/04/2023
CAMEL: Co-Designing AI Models and Embedded DRAMs for Efficient On-Device Learning
The emergence of the Internet of Things (IoT) has resulted in a remarkab...
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05/02/2023
Design Space Exploration and Optimization for Carbon-Efficient Extended Reality Systems
As computing hardware becomes more specialized, designing environmentall...
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02/21/2023
MP-Rec: Hardware-Software Co-Design to Enable Multi-Path Recommendation
Deep learning recommendation systems serve personalized content under di...
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01/26/2023
PerfSAGE: Generalized Inference Performance Predictor for Arbitrary Deep Learning Models on Edge Devices
The ability to accurately predict deep neural network (DNN) inference pe...
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01/26/2023
GPU-based Private Information Retrieval for On-Device Machine Learning Inference
On-device machine learning (ML) inference can enable the use of private ...
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12/01/2022
Architectural Implications of Embedding Dimension during GCN on CPU and GPU
Graph Neural Networks (GNNs) are a class of neural networks designed to ...
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05/13/2022
Impala: Low-Latency, Communication-Efficient Private Deep Learning Inference
This paper proposes Impala, a new cryptographic protocol for private inf...
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05/06/2022
OMU: A Probabilistic 3D Occupancy Mapping Accelerator for Real-time OctoMap at the Edge
Autonomous machines (e.g., vehicles, mobile robots, drones) require soph...
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03/05/2022
Tabula: Efficiently Computing Nonlinear Activation Functions for Secure Neural Network Inference
Multiparty computation approaches to secure neural network inference tra...
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03/01/2022
Specialized Accelerators and Compiler Flows: Replacing Accelerator APIs with a Formal Software/Hardware Interface
Specialized accelerators are increasingly used to meet the power-perform...
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01/21/2022
Trireme: Exploring Hierarchical Multi-Level Parallelism for Domain Specific Hardware Acceleration
The design of heterogeneous systems that include domain specific acceler...
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11/17/2021
Early DSE and Automatic Generation of Coarse Grained Merged Accelerators
Post-Moore's law area-constrained systems rely on accelerators to delive...
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09/02/2021
NVMExplorer: A Framework for Cross-Stack Comparisons of Embedded Non-Volatile Memories
Repeated off-chip memory accesses to DRAM drive up operating power for d...
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06/18/2021
Application-driven Design Exploration for Dense Ferroelectric Embedded Non-volatile Memories
The memory wall bottleneck is a key challenge across many data-intensive...
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06/10/2021
Gradient Disaggregation: Breaking Privacy in Federated Learning by Reconstructing the User Participant Matrix
We show that aggregated model updates in federated learning may be insec...
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05/18/2021
RecPipe: Co-designing Models and Hardware to Jointly Optimize Recommendation Quality and Performance
Deep learning recommendation systems must provide high quality, personal...
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05/03/2021
Quantifying and Maximizing the Benefits of Back-End Noise Adaption on Attention-Based Speech Recognition Models
This work analyzes how attention-based Bidirectional Long Short-Term Mem...
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02/05/2021
Machine Learning-Based Automated Design Space Exploration for Autonomous Aerial Robots
Building domain-specific architectures for autonomous aerial robots is c...
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01/29/2021
RecSSD: Near Data Processing for Solid State Drive Based Recommendation Inference
Neural personalized recommendation models are used across a wide variety...
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11/28/2020
EdgeBERT: Sentence-Level Energy Optimizations for Latency-Aware Multi-Task NLP Inference
Transformer-based language models such as BERT provide significant accur...
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10/28/2020
Chasing Carbon: The Elusive Environmental Footprint of Computing
Given recent algorithm, software, and hardware innovation, computing has...
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10/10/2020
Cross-Stack Workload Characterization of Deep Recommendation Systems
Deep learning based recommendation systems form the backbone of most per...
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05/31/2020
Cheetah: Optimizations and Methods for PrivacyPreserving Inference via Homomorphic Encryption
As the application of deep learning continues to grow, so does the amoun...
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01/13/2020
CHIPKIT: An agile, reusable open-source framework for rapid test chip development
The current trend for domain-specific architectures (DSAs) has led to re...
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01/08/2020
DeepRecSys: A System for Optimizing End-To-End At-scale Neural Recommendation Inference
Neural personalized recommendation is the corner-stone of a wide collect...
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12/10/2019
SMAUG: End-to-End Full-Stack Simulation Infrastructure for Deep Learning Workloads
In recent years, there has been tremendous advances in hardware accelera...
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11/30/2019
A binary-activation, multi-level weight RNN and training algorithm for processing-in-memory inference with eNVM
We present a new algorithm for training neural networks with binary acti...
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10/02/2019
MLPerf Training Benchmark
Machine learning is experiencing an explosion of software and hardware s...
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09/29/2019
AdaptivFloat: A Floating-point based Data Type for Resilient Deep Learning Inference
Conventional hardware-friendly quantization methods, such as fixed-point...
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07/24/2019
Benchmarking TPU, GPU, and CPU Platforms for Deep Learning
Training deep learning models is compute-intensive and there is an indus...
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05/24/2019
Learning Low-Rank Approximation for CNNs
Low-rank approximation is an effective model compression technique to no...
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05/24/2019
Structured Compression by Unstructured Pruning for Sparse Quantized Neural Networks
Model compression techniques, such as pruning and quantization, are beco...
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05/14/2019
Network Pruning for Low-Rank Binary Indexing
Pruning is an efficient model compression technique to remove redundancy...
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02/15/2018
Cloud No Longer a Silver Bullet, Edge to the Rescue
This paper takes the position that, while cognitive computing today reli...
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11/13/2017
