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Found 19 papers, 6 papers with code
A Comprehensive Survey of Convolutions in Deep Learning: Applications, Challenges, and Future Trends
In today's digital age, Convolutional Neural Networks (CNNs), a subset of Deep Learning (DL), are widely used for various computer vision tasks such as image classification, object detection, and image segmentation.
TransAxx: Efficient Transformers with Approximate Computing
Vision Transformer (ViT) models which were recently introduced by the transformer architecture have shown to be very competitive and often become a popular alternative to Convolutional Neural Networks (CNNs).
Hardware-Aware DNN Compression via Diverse Pruning and Mixed-Precision Quantization
We explore, for the first time, per-layer fine- and coarse-grained pruning, in the same DNN architecture, in addition to low bit-width mixed-precision quantization for weights and activations.
Federated Learning for Computationally-Constrained Heterogeneous Devices: A Survey
With an increasing number of smart devices like internet of things (IoT) devices deployed in the field, offloadingtraining of neural networks (NNs) to a central server becomes more and more infeasible.
Aggregating Capacity in FL through Successive Layer Training for Computationally-Constrained Devices
If the required memory to train a model exceeds this limit, the device will be excluded from the training.
Model-to-Circuit Cross-Approximation For Printed Machine Learning Classifiers
Printed electronics (PE) promises on-demand fabrication, low non-recurring engineering costs, and sub-cent fabrication costs.
Co-Design of Approximate Multilayer Perceptron for Ultra-Resource Constrained Printed Circuits
Printed Electronics (PE) exhibits on-demand, extremely low-cost hardware due to its additive manufacturing process, enabling machine learning (ML) applications for domains that feature ultra-low cost, conformity, and non-toxicity requirements that silicon-based systems cannot deliver.
Approximate Computing and the Efficient Machine Learning Expedition
In this work, we enlighten the synergistic nature of AxC and ML and elucidate the impact of AxC in designing efficient ML systems.
Energy-efficient DNN Inference on Approximate Accelerators Through Formal Property Exploration
Deep Neural Networks (DNNs) are being heavily utilized in modern applications and are putting energy-constraint devices to the test.
Hardware Approximate Techniques for Deep Neural Network Accelerators: A Survey
To enable efficient execution of DNN inference, more and more research works, therefore, exploit the inherent error resilience of DNNs and employ Approximate Computing (AC) principles to address the elevated energy demands of DNN accelerators.
Cross-Layer Approximation For Printed Machine Learning Circuits
In our extensive experimental evaluation we consider 14 MLPs and SVMs and evaluate more than 4300 approximate and exact designs.
CoCoFL: Communication- and Computation-Aware Federated Learning via Partial NN Freezing and Quantization
To adapt to the devices' heterogeneous resources, CoCoFL freezes and quantizes selected layers, reducing communication, computation, and memory requirements, whereas other layers are still trained in full precision, enabling to reach a high accuracy.
AdaPT: Fast Emulation of Approximate DNN Accelerators in PyTorch
Current state-of-the-art employs approximate multipliers to address the highly increased power demands of DNN accelerators.
DISTREAL: Distributed Resource-Aware Learning in Heterogeneous Systems
We study the problem of distributed training of neural networks (NNs) on devices with heterogeneous, limited, and time-varying availability of computational resources.
Positive/Negative Approximate Multipliers for DNN Accelerators
In addition, we propose a filter-oriented approximation method to map the weights to the appropriate modes of the approximate multiplier.
Reliability-Aware Quantization for Anti-Aging NPUs
Transistor aging is one of the major concerns that challenges designers in advanced technologies.
Control Variate Approximation for DNN Accelerators
In this work, we introduce a control variate approximation technique for low error approximate Deep Neural Network (DNN) accelerators.
Distributed Learning on Heterogeneous Resource-Constrained Devices
We consider a distributed system, consisting of a heterogeneous set of devices, ranging from low-end to high-end.
Hardware Trojan Detection Using Controlled Circuit Aging
Combining IC aging with over-clocking produces a pattern of bit errors at the IC output by the induced timing violations.
