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.
Review of efficient neural networks for TinyML on resource-constrained devices.
The growing number of low-power smart devices in the Internet of Things is coupled with the concept of "Edge Computing", that is moving some of the intelligence, especially machine learning, towards the edge of the network. Enabling machine learning algorithms to run on resource-constrained hardware, typically on low-p…
SEFR is a fast, energy-efficient classifier for ultra-low power devices.
Designing deep learning models for highly-constrained hardware would allow imbuing many edge devices with intelligence. Microcontrollers (MCUs) are an attractive platform for building smart devices due to their low cost, wide availability, and modest power usage. However, they lack the computational resources to run ne…
Improved deep learning model deployment on tiny MCUs with mixed-precision quantization.
MCU-Net combines U-Net and Monte Carlo Dropout for uncertainty in medical image segmentation.
This work optimizes DNN inference for energy-harvesting devices by compressing and selectively executing neural network exits.
There is growing interest in being able to run neural networks on sensors, wearables and internet-of-things (IoT) devices. However, the computational demands of neural networks make them difficult to deploy on resource-constrained edge devices. To meet this need, our work introduces a new recurrent unit architecture th…
This paper tackles energy-efficient machine learning on low-power devices.
Computer vision performances have been significantly improved in recent years by Convolutional Neural Networks(CNN). Currently, applications using CNN algorithms are deployed mainly on general purpose hardwares, such as CPUs, GPUs or FPGAs. However, power consumption, speed, accuracy, memory footprint, and die size sho…
Implantable, closed-loop devices for automated early detection and stimulation of epileptic seizures are promising treatment options for patients with severe epilepsy that cannot be treated with traditional means. Most approaches for early seizure detection in the literature are, however, not optimized for implementati…
In natural hazard warning systems fast decision making is vital to avoid catastrophes. Decision making at the edge of a wireless sensor network promises fast response times but is limited by the availability of energy, data transfer speed, processing and memory constraints. In this work we present a realization of a wi…
Automated tool reduces FPGA inference latency to 5 μs for deep neural networks.
PolyLUT uses polynomials to reduce FPGA latency.
Recently, the posit numerical format has shown promise for DNN data representation and compute with ultra-low precision ([5..8]-bit). However, majority of studies focus only on DNN inference. In this work, we propose DNN training using posits and compare with the floating point training. We evaluate on both MNIST and F…
TinyLSTMs reduces speech enhancement model size and latency for hearing aids.
Intelligent transportation systems (ITSs) will be a major component of tomorrow's smart cities. However, realizing the true potential of ITSs requires ultra-low latency and reliable data analytics solutions that can combine, in real-time, a heterogeneous mix of data stemming from the ITS network and its environment. Su…
Efforts to reduce the numerical precision of computations in deep learning training have yielded systems that aggressively quantize weights and activations, yet employ wide high-precision accumulators for partial sums in inner-product operations to preserve the quality of convergence. The absence of any framework to an…
Improves bit error tolerance in RRAM-based BNNs without overfitting.
New framework reduces LLM complexity by directly finetuning in Boolean domain.
WrapNet optimizes inference for low-resolution neural networks by using 8-bit additions.
GPU-accelerates multiuser detection for 5G URLLC systems.
Characterizing statistical properties of solutions of inverse problems is essential for decision making. Bayesian inversion offers a tractable framework for this purpose, but current approaches are computationally unfeasible for most realistic imaging applications in the clinic. We introduce two novel deep learning bas…
A major challenge in computed tomography (CT) is to reduce X-ray dose to a low or even ultra-low level while maintaining the high quality of reconstructed images. We propose a new method for CT reconstruction that combines penalized weighted-least squares reconstruction (PWLS) with regularization based on a sparsifying…
Low-precision DNNs have been extensively explored in order to reduce the size of DNN models for edge devices. Recently, the posit numerical format has shown promise for DNN data representation and compute with ultra-low precision in [5..8]-bits. However, previous studies were limited to studying posit for DNN inference…
Paper presents FPGA implementation for efficient recurrent neural networks.
Currently, pension providers are running into trouble mainly due to the ultra-low interest rates and the guarantees associated to some pension benefits. With the aim of reducing the pension volatility and providing adequate pension levels with no guarantees, we carry out mathematical analysis of a new pension design in…
In this paper, we consider the problem of fast and efficient indexing techniques for sequences evolving in non-Euclidean spaces. This problem has several applications in the areas of human activity analysis, where there is a need to perform fast search, and recognition in very high dimensional spaces. The problem is ma…
A new high-frequency market making strategy using Deep Hawkes process.
STARK improves denoising of low-depth spatial transcriptomics images.
Generative compression technique reduces neural network size and improves performance on microcontrollers.
Framework predicts and prepares for rain-induced microwave link attenuation.
New algorithm extracts device profiles for short-term power predictions in commercial buildings.
Power quandles improve group invariants and allow group presentations.
Novel power transform unifies various mathematical functions.
Study of metrics on positive-definite matrices from power potential, linking to power means.
The paper establishes conditions for strict power concavity in convolutions.
Power system studies require the topological structures of real-world power networks; however, such data is confidential due to important security concerns. Thus, power grid synthesis (PGS), i.e., creating realistic power grids that imitate actual power networks, has gained significant attention. In this letter, we cas…
Power laws detected in financial data, modeled with random multipliers.
Bayesian method models multivalued power data from wind farms.
WindDragon forecasts wind power with deep learning.
Paper examines power consumption in neural networks using various activation functions.
Paper introduces reinforcement learning for managing power grids.
Study of origamis' singularities for groups of prime-power order.
Photovoltaic systems have been widely deployed in recent times to meet the increased electricity demand as an environmental-friendly energy source. The major challenge for integrating photovoltaic systems in power systems is the unpredictability of the solar power generated. In this paper, we analyze the impact of havi…
Auto-regressive conditionally heteroskedastic (ARCH) family models are still used, by practitioners in business and economic policy making, as a conditional volatility forecasting models. Furthermore ARCH models still are attracting an interest of the researchers. In this contribution we consider the well known GARCH(1…
Survey on GNNs' power and limitations.
GP CC-OPF solves uncertain power grid optimization with Gaussian Process.