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.
HarDNN detects and protects CNNs from hardware errors.
Applying deep neural networks (DNNs) in mobile and safety-critical systems, such as autonomous vehicles, demands a reliable and efficient execution on hardware. Optimized dedicated hardware accelerators are being developed to achieve this. However, the design of efficient and reliable hardware has become increasingly d…
Quantum algorithm reduces CVA risk-neutral expectation estimation costs.
New method bounds hardware noise without assumptions.
The intrinsic error tolerance of neural network (NN) makes approximate computing a promising technique to improve the energy efficiency of NN inference. Conventional approximate computing focuses on balancing the efficiency-accuracy trade-off for existing pre-trained networks, which can lead to suboptimal solutions. In…
Extends phase retrieval methods to handle sensing vector errors.
StatQAT optimizes quantization for deep networks, reducing computational cost and memory usage.
A scalable DL benchmarking platform for evaluating and comparing models, frameworks, and hardware.
Survey of QML applications on near-term quantum devices.
Mitigates faults in DNNs by clipping activation values, improving their resilience.
PredPCA extracts key components for better time series prediction.
In recent years, Convolutional Neural Network (CNN) based methods have achieved great success in a large number of applications and have been among the most powerful and widely used techniques in computer vision. However, CNN-based methods are computational-intensive and resource-consuming, and thus are hard to be inte…
Paper tackles noisy neural networks and proposes a method to enhance their robustness.
The paper offers error bounds for quantized dynamical models.
Quantum reservoir computing improves volatility forecasting.
We trained three Binarized Convolutional Neural Network architectures (LeNet-4, Network-In-Network, AlexNet) on a variety of datasets (MNIST, CIFAR-10, CIFAR-100, extended SVHN, ImageNet) using error-prone activations and tested them without errors to study the resilience of the training process. With the exception of …
Study shows BNN inference accelerators are vulnerable to soft errors, causing significant misclassification.
Bio-inspired neuromorphic hardware is a research direction to approach brain's computational power and energy efficiency. Spiking neural networks (SNN) encode information as sparsely distributed spike trains and employ spike-timing-dependent plasticity (STDP) mechanism for learning. Existing hardware implementations of…
Quantum neural networks can approximate noisy functions accurately.
This paper presents a methodology for selecting the mini-batch size that minimizes Stochastic Gradient Descent (SGD) learning time for single and multiple learner problems. By decoupling algorithmic analysis issues from hardware and software implementation details, we reveal a robust empirical inverse law between mini-…
A methodology for resilience analysis of Capsule Networks under approximation errors.
New KWS neural networks improve accuracy and power efficiency.
Recent machine learning methods use increasingly large deep neural networks to achieve state of the art results in various tasks. The gains in performance come at the cost of a substantial increase in computation and storage requirements. This makes real-time implementations on limited resources hardware a challenging …
Deep learning architectures (DLA) have shown impressive performance in computer vision, natural language processing and so on. Many DLA make use of cloud computing to achieve classification due to the high computation and memory requirements. Privacy and latency concerns resulting from cloud computing has inspired the …
Benchmark for DL inference on embedded HWAs, focusing on autonomous driving.
New MIMO constellation design for noncoherent communications reduces hardware complexity.
New Ising models improve consensus clustering on specialized hardware.
Neural architecture search (NAS) has a great impact by automatically designing effective neural network architectures. However, the prohibitive computational demand of conventional NAS algorithms (e.g. GPU hours) makes it difficult to \emph{directly} search the architectures on large-scale tasks (e.g. ImageNet).…
DANCE optimizes neural network and accelerator design for faster, more efficient DNN execution.
Recent breakthroughs in Deep Learning (DL) applications have made DL models a key component in almost every modern computing system. The increased popularity of DL applications deployed on a wide-spectrum of platforms have resulted in a plethora of design challenges related to the constraints introduced by the hardware…
Quantum algorithms for CVaR portfolio optimization face trade-offs between hardware coherence and expressibility.
Method predicts hardware resource usage by control software with guaranteed linear convergence.
High-fidelity quantum simulations demonstrated on short-coherence hardware.
Approximate computing is being considered as a promising design paradigm to overcome the energy and performance challenges in computationally demanding applications. If the case where the accuracy can be configured, the quality level versus energy efficiency or delay also may be traded-off. For this technique to be use…
This paper tackles co-design of neural hardware and software to improve efficiency.
The implementation of artificial neural networks in hardware substrates is a major interdisciplinary enterprise. Well suited candidates for physical implementations must combine nonlinear neurons with dedicated and efficient hardware solutions for both connectivity and training. Reservoir computing addresses the proble…
We derive scaling laws for optimizing neural networks in hardware.
GOBO compresses 99.9% of BERT model parameters to 3 bits, improving inference efficiency.
This paper highlights new opportunities for designing large-scale machine learning systems as a consequence of blurring traditional boundaries that have allowed algorithm designers and application-level practitioners to stay -- for the most part -- oblivious to the details of the underlying hardware-level implementatio…
Optimized neural networks for Edge TPU achieve high accuracy in real-time image classification.
Recent advances in deep neural networks (DNNs) owe their success to training algorithms that use backpropagation and gradient-descent. Backpropagation, while highly effective on von Neumann architectures, becomes inefficient when scaling to large networks. Commonly referred to as the weight transport problem, each neur…
With the rising popularity of machine learning and the ever increasing demand for computational power, there is a growing need for hardware optimized implementations of neural networks and other machine learning models. As the technology evolves, it is also plausible that machine learning or artificial intelligence wil…
VegasFlow accelerates complex simulations across various hardware platforms.
The ever increasing computational cost of Deep Neural Networks (DNN) and the demand for energy efficient hardware for DNN acceleration has made accuracy and hardware cost co-optimization for DNNs tremendously important, especially for edge devices. Owing to the large parameter space and cost of evaluating each paramete…
Paper presents efficient algorithms for convolutional neural networks using Winograd minimal filtering.
Hardware-accelerated RBM solves large combinatorial problems and integer factorization.
Deep neural networks (DNNs) frequently contain far more weights, represented at a higher precision, than are required for the specific task which they are trained to perform. Consequently, they can often be compressed using techniques such as weight pruning and quantization that reduce both the model size and inference…
Improves VQAs by balancing classical and quantum training resources.