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Found 16 papers, 7 papers with code
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).
Embedding Hardware Approximations in Discrete Genetic-based Training for Printed MLPs
Due to the discrete nature of hardware approximation, we propose and implement a genetic-based, approximate, hardware-aware training approach specifically designed for printed MLPs.
Bespoke Approximation of Multiplication-Accumulation and Activation Targeting Printed Multilayer Perceptrons
Printed Electronics (PE) feature distinct and remarkable characteristics that make them a prominent technology for achieving true ubiquitous computing.
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.
On-sensor Printed Machine Learning Classification via Bespoke ADC and Decision Tree Co-Design
Printed electronics (PE) technology provides cost-effective hardware with unmet customization, due to their low non-recurring engineering and fabrication costs.
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.
Approximate Decision Trees For Machine Learning Classification on Tiny Printed Circuits
Although Printed Electronics (PE) cannot compete with silicon-based systems in conventional evaluation metrics, e. g., integration density, area and performance, PE offers attractive properties such as on-demand ultra-low-cost fabrication, flexibility and non-toxicity.
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.
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.
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.
