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05/31/2023
Incremental Randomized Smoothing Certification
Randomized smoothing-based certification is an effective approach for ob...
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05/18/2023
Black-Box Targeted Reward Poisoning Attack Against Online Deep Reinforcement Learning
We propose the first black-box targeted attack against online deep reinf...
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04/04/2023
Incremental Verification of Neural Networks
Complete verification of deep neural networks (DNNs) can exactly determi...
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03/06/2023
SPARTA: Spatial Acceleration for Efficient and Scalable Horizontal Diffusion Weather Stencil Computation
Fast and accurate climate simulations and weather predictions are critic...
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02/14/2023
COMET: X86 Cost Model Explanation Framework
ML-based program cost models have been shown to yield fairly accurate pr...
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01/31/2023
Interpreting Robustness Proofs of Deep Neural Networks
In recent years numerous methods have been developed to formally verify ...
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12/09/2022
TargetCall: Eliminating the Wasted Computation in Basecalling via Pre-Basecalling Filtering
Basecalling is an essential step in nanopore sequencing analysis where t...
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11/06/2022
A Framework for Designing Efficient Deep Learning-Based Genomic Basecallers
Nanopore sequencing generates noisy electrical signals that need to be c...
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08/22/2022
LEAPER: Fast and Accurate FPGA-based System Performance Prediction via Transfer Learning
Machine learning has recently gained traction as a way to overcome the s...
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08/18/2022
Designing, Modeling, and Optimizing Data-Intensive Computing Systems
The cost of moving data between the memory units and the compute units i...
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07/22/2022
Training Certifiably Robust Neural Networks Against Semantic Perturbations
Semantic image perturbations, such as scaling and rotation, have been sh...
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07/16/2022
An Experimental Evaluation of Machine Learning Training on a Real Processing-in-Memory System
Training machine learning (ML) algorithms is a computationally intensive...
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06/22/2022
Robust Universal Adversarial Perturbations
Universal Adversarial Perturbations (UAPs) are imperceptible, image-agno...
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06/13/2022
Machine Learning Training on a Real Processing-in-Memory System
Training machine learning algorithms is a computationally intensive proc...
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05/30/2022
Efficient Reward Poisoning Attacks on Online Deep Reinforcement Learning
We study data poisoning attacks on online deep reinforcement learning (D...
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05/16/2022
Going From Molecules to Genomic Variations to Scientific Discovery: Intelligent Algorithms and Architectures for Intelligent Genome Analysis
We now need more than ever to make genome analysis more intelligent. We ...
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05/15/2022
Sibyl: Adaptive and Extensible Data Placement in Hybrid Storage Systems Using Online Reinforcement Learning
Hybrid storage systems (HSS) use multiple different storage devices to p...
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05/12/2022
SeGraM: A Universal Hardware Accelerator for Genomic Sequence-to-Graph and Sequence-to-Sequence Mapping
A critical step of genome sequence analysis is the mapping of sequenced ...
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03/07/2022
Scalable Verification of GNN-based Job Schedulers
Recently, Graph Neural Networks (GNNs) have been applied for scheduling ...
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12/28/2021
Casper: Accelerating Stencil Computation using Near-cache Processing
Stencil computation is one of the most used kernels in a wide variety of...
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10/13/2021
Language Modelling via Learning to Rank
We consider language modelling (LM) as a multi-label structured predicti...
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09/01/2021
Shared Certificates for Neural Network Verification
Existing neural network verifiers compute a proof that each input is han...
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07/19/2021
Accelerating Weather Prediction using Near-Memory Reconfigurable Fabric
Ongoing climate change calls for fast and accurate weather and climate m...
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06/11/2021
FPGA-Based Near-Memory Acceleration of Modern Data-Intensive Applications
Modern data-intensive applications demand high computation capabilities ...
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03/30/2021
Robustness Certification for Point Cloud Models
The use of deep 3D point cloud models in safety-critical applications, s...
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03/17/2021
Secure Hypersphere Range Query on Encrypted Data
Spatial queries like range queries, nearest neighbor, circular range que...
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03/05/2021
Precise Multi-Neuron Abstractions for Neural Network Certification
Formal verification of neural networks is critical for their safe adopti...
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02/02/2021
pseudo-Bayesian Neural Networks for detecting Out of Distribution Inputs
Conventional Bayesian Neural Networks (BNNs) are known to be capable of ...
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09/17/2020
NERO: A Near High-Bandwidth Memory Stencil Accelerator for Weather Prediction Modeling
Ongoing climate change calls for fast and accurate weather and climate m...
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07/23/2020
Scalable Inference of Symbolic Adversarial Examples
We present a novel method for generating symbolic adversarial examples: ...
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07/20/2020
Neural Network Robustness Verification on GPUs
Certifying the robustness of neural networks against adversarial attacks...
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07/15/2020
Predicting Mechanical Ventilation Requirement and Mortality in COVID-19 using Radiomics and Deep Learning on Chest Radiographs: A Multi-Institutional Study
Objectives: To predict mechanical ventilation requirement and mortality ...
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06/30/2020
TDO-CIM: Transparent Detection and Offloading for Computation In-memory
Computation in-memory is a promising non-von Neumann approach aiming at ...
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06/16/2020
Can tumor location on pre-treatment MRI predict likelihood of pseudo-progression versus tumor recurrence in Glioblastoma? A feasibility study
A significant challenge in Glioblastoma (GBM) management is identifying ...
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05/27/2020
Fast and Effective Robustness Certification for Recurrent Neural Networks
We present a precise and scalable verifier for recurrent neural networks...
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04/20/2020
Agile Autotuning of a Transprecision Tensor Accelerator Overlay for TVM Compiler Stack
Specialized accelerators for tensor-operations, such as blocked-matrix o...
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03/08/2020
Adversarial Attacks on Probabilistic Autoregressive Forecasting Models
We develop an effective generation of adversarial attacks on neural mode...
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08/07/2019
Near-Memory Computing: Past, Present, and Future
The conventional approach of moving data to the CPU for computation has ...
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06/24/2019
Platform Independent Software Analysis for Near Memory Computing
Near-memory Computing (NMC) promises improved performance for the applic...
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04/18/2019
Memory and Parallelism Analysis Using a Platform-Independent Approach
Emerging computing architectures such as near-memory computing (NMC) pro...
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03/29/2019
A Provable Defense for Deep Residual Networks
We present a training system, which can provably defend significantly la...
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01/15/2018
