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Approximate Computing Survey, Part II: Application-Specific & Architectural Approximation Techniques and Applications
The challenging deployment of compute-intensive applications from domains such Artificial Intelligence (AI) and Digital Signal Processing (DSP), forces the community of computing systems to explore new design approaches.
Evaluating and Enhancing Robustness of Deep Recommendation Systems Against Hardware Errors
Deep recommendation systems (DRS) heavily depend on specialized HPC hardware and accelerators to optimize energy, efficiency, and recommendation quality.
PyGFI: Analyzing and Enhancing Robustness of Graph Neural Networks Against Hardware Errors
Inspired by the inherent algorithmic resilience of DL methods, this paper conducts, for the first time, a large-scale and empirical study of GNN resilience, aiming to understand the relationship between hardware faults and GNN accuracy.
NasHD: Efficient ViT Architecture Performance Ranking using Hyperdimensional Computing
Neural Architecture Search (NAS) is an automated architecture engineering method for deep learning design automation, which serves as an alternative to the manual and error-prone process of model development, selection, evaluation and performance estimation.
Hyperdimensional Computing vs. Neural Networks: Comparing Architecture and Learning Process
Hyperdimensional Computing (HDC) has obtained abundant attention as an emerging non von Neumann computing paradigm.
EnHDC: Ensemble Learning for Brain-Inspired Hyperdimensional Computing
Ensemble learning is a classical learning method utilizing a group of weak learners to form a strong learner, which aims to increase the accuracy of the model.
Automated Architecture Search for Brain-inspired Hyperdimensional Computing
On the Clintox dataset, which tries to learn features from developed drugs that passed/failed clinical trials for toxicity reasons, the searched HDC architecture obtains the state-of-the-art ROC-AUC scores, which are 0. 80% higher than the manually designed HDC and 9. 75% higher than conventional neural networks.
HDCoin: A Proof-of-Useful-Work Based Blockchain for Hyperdimensional Computing
We formulate the model development of HDC as a problem that can be used in blockchain mining.
MoleHD: Ultra-Low-Cost Drug Discovery using Hyperdimensional Computing
In this paper, we propose a viable alternative to existing learning methods by presenting MoleHD, a method based on brain-inspired hyperdimensional computing (HDC) for molecular property prediction.
HDXplore: Automated Blackbox Testing of Brain-Inspired Hyperdimensional Computing
In this paper, we systematically expose the unexpected or incorrect behaviors of HDC models by developing HDXplore, a blackbox differential testing-based framework.
HDTest: Differential Fuzz Testing of Brain-Inspired Hyperdimensional Computing
Using handwritten digit classification as an example, we show that HDTest can generate thousands of adversarial inputs with negligible perturbations that can successfully fool HDC models.
Deep Learning Based Walking Tasks Classification in Older Adults using fNIRS
However, the automatic classification of differences in cognitive activations under single and dual task gait conditions has not been extensively studied yet.
AMITE: A Novel Polynomial Expansion for Analyzing Neural Network Nonlinearities
Polynomial expansions are important in the analysis of neural network nonlinearities.
EnFuzz: From Ensemble Learning to Ensemble Fuzzing
For evaluation, we implement EnFuzz , a prototype basing on four strong open-source fuzzers (AFL, AFLFast, AFLGo, FairFuzz), and test them on Google's fuzzing test suite, which consists of widely used real-world applications.
Software Engineering
