A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Optimizes energy efficiency in wireless sensor networks with limited information.
Quantum computing offers energy savings over classical computing.
Enhanced tabular benchmarks for energy-efficient neural architecture search.
The energy efficiency of neuromorphic hardware is greatly affected by the energy of storing, accessing, and updating synaptic parameters. Various methods of memory organisation targeting energy-efficient digital accelerators have been investigated in the past, however, they do not completely encapsulate the energy cost…
EDCompress optimizes energy efficiency of CNN models on edge devices.
Inference problems in graphical models can be represented as a constrained optimization of a free energy function. It is known that when the Bethe free energy is used, the fixedpoints of the belief propagation (BP) algorithm correspond to the local minima of the free energy. However BP fails to converge in many cases o…
A new energy-efficient pruning method for federated learning.
mAIS improves free energy evaluation efficiency.
SmartDeal reduces energy and storage costs for deep neural networks.
This paper tackles energy-efficient machine learning on low-power devices.
New sampler tackles complex discrete energy landscapes efficiently.
Designing energy-efficient networks is of critical importance for enabling state-of-the-art deep learning in mobile and edge settings where the computation and energy budgets are highly limited. Recently, Liu et al. (2019) framed the search of efficient neural architectures into a continuous splitting process: it itera…
E2GC optimizes energy efficiency in DNNs by balancing computational and data movement costs.
Energy-efficient sampling for machine learning using magnetic tunnel junctions.
AI methods are energy-intensive, but efficiency alone isn't enough for sustainability.
This thesis tackles NILM challenges with a new dataset and efficient edge deployment techniques.
Energy savings for DNN inference on resource-constrained devices.
Recurrent Neural Networks (RNNs) are becoming increasingly important for time series-related applications which require efficient and real-time implementations. The recent pruning based work ESE suffers from degradation of performance/energy efficiency due to the irregular network structure after pruning. We propose bl…
Bayesian model for energy consumption helps electric vehicles navigate efficiently.
Optimal design portfolios improve energy efficiency and reduce risk in uncertain reservoirs.
SEFR is a fast, energy-efficient classifier for ultra-low power devices.
Machine Learning algorithms based on Brain-inspired Hyperdimensional(HD) computing imitate cognition by exploiting statistical properties of high-dimensional vector spaces. It is a promising solution for achieving high energy efficiency in different machine learning tasks, such as classification, semi-supervised learni…
Energy-efficient DL inference for IoT devices reduces power consumption and improves performance.
New system assigns vehicles to routes for cost and energy efficiency.
Energy-efficient navigation constitutes an important challenge in electric vehicles, due to their limited battery capacity. We employ a Bayesian approach to model the energy consumption at road segments for efficient navigation. In order to learn the model parameters, we develop an online learning framework and investi…
Machine Learning (ML) algorithms, like Convolutional Neural Networks (CNN), Support Vector Machines (SVM), etc. have become widespread and can achieve high statistical performance. However their accuracy decreases significantly in energy-constrained mobile and embedded systems space, where all computations need to be c…
This paper analyzes energy and carbon footprints in distributed and federated learning.
"How much energy is consumed for an inference made by a convolutional neural network (CNN)?" With the increased popularity of CNNs deployed on the wide-spectrum of platforms (from mobile devices to workstations), the answer to this question has drawn significant attention. From lengthening battery life of mobile device…
This paper generalizes neural transport learning for free energy estimation in arbitrary state spaces.
Compact DNNs increase memory footprint and reduce energy efficiency.
In this paper, we propose a gamification approach as a novel framework for smart building infrastructure with the goal of motivating human occupants to reconsider personal energy usage and to have positive effects on their environment. Human interaction in the context of cyber-physical systems is a core component and c…
Efficiently samples and learns densities with symmetries using equivariant methods.
Deep neural networks have demonstrated state-of-the-art performance on many classification tasks. However, they have no inherent capability to recognize when their predictions are wrong. There have been several efforts in the recent past to detect natural errors but the suggested mechanisms pose additional energy requi…
Traditional centralized energy systems have the disadvantages of difficult management and insufficient incentives. Blockchain is an emerging technology, which can be utilized in energy systems to enhance their management and control. Integrating token economy and blockchain technology, token economic systems in energy …
New deep learning model optimizes energy use in buildings.
A hybrid neural network optimizes AI deployment on edge and cloud for energy efficiency.
VAV method optimizes learning rate for faster, stable SGD convergence.
EWFM trains continuous flows with only energy evaluations, improving sample quality with fewer computations.
In this paper, we design a navigation policy for multiple unmanned aerial vehicles (UAVs) where mobile base stations (BSs) are deployed to improve the data freshness and connectivity to the Internet of Things (IoT) devices. First, we formulate an energy-efficient trajectory optimization problem in which the objective i…
This paper introduces intermittent learning - the goal of which is to enable energy harvested computing platforms capable of executing certain classes of machine learning tasks effectively and efficiently. We identify unique challenges to intermittent learning relating to the data and application semantics of machine l…
Convolutional neural networks (CNNs) have been increasingly deployed to edge devices. Hence, many efforts have been made towards efficient CNN inference in resource-constrained platforms. This paper attempts to explore an orthogonal direction: how to conduct more energy-efficient training of CNNs, so as to enable on-de…
Investigations have been performed into using clustering methods in data mining time-series data from smart meters. The problem is to identify patterns and trends in energy usage profiles of commercial and industrial customers over 24-hour periods, and group similar profiles. We tested our method on energy usage data p…
Stella Nera accelerates matrix multiplications with a hash-based approach, achieving high energy efficiency and accuracy.
Carbontracker tracks and predicts training DL models' carbon footprint.
Energy game-theoretic frameworks have emerged to be a successful strategy to encourage energy efficient behavior in large scale by leveraging human-in-the-loop strategy. A number of such frameworks have been introduced over the years which formulate the energy saving process as a competitive game with appropriate incen…
Software Defined Networking (SDN) achieves programmability of a network through separation of the control and data planes. It enables flexibility in network management and control. Energy efficiency is one of the challenging global problems which has both economic and environmental impact. A massive amount of informati…
iEFM trains CNF models from unnormalized densities efficiently.
BNEM improves Boltzmann sampler efficiency.