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Found 9 papers, 1 papers with code
Photonics for Sustainable Computing
Our analysis shows that photonics can reduce both operational and embodied carbon footprints with its high energy efficiency and at least 4$\times$ less fabrication carbon cost per unit area than 28 nm CMOS.
Towards Efficient Hyperdimensional Computing Using Photonics
Over the past few years, silicon photonics-based computing has emerged as a promising alternative to CMOS-based computing for Deep Neural Networks (DNN).
Accelerating DNN Training With Photonics: A Residue Number System-Based Design
Photonic computing is a compelling avenue for performing highly efficient matrix multiplication, a crucial operation in Deep Neural Networks (DNNs).
Photonic Accelerators for Image Segmentation in Autonomous Driving and Defect Detection
Photonic computing promises faster and more energy-efficient deep neural network (DNN) inference than traditional digital hardware.
A Blueprint for Precise and Fault-Tolerant Analog Neural Networks
Our study demonstrates that analog accelerators utilizing the RNS-based approach can achieve ${\geq}99\%$ of FP32 accuracy for state-of-the-art DNN inference using data converters with only $6$-bit precision whereas a conventional analog core requires more than $8$-bit precision to achieve the same accuracy in the same DNNs.
INT-FP-QSim: Mixed Precision and Formats For Large Language Models and Vision Transformers
To supplement this ongoing effort, we propose INT-FP-QSim: an open-source simulator that enables flexible evaluation of LLMs and vision transformers at various numerical precisions and formats.
Leveraging Residue Number System for Designing High-Precision Analog Deep Neural Network Accelerators
In addition, we show that RNS can reduce the energy consumption of the data converters within an analog accelerator by several orders of magnitude compared to a regular fixed-point approach.
Sensitivity-Aware Finetuning for Accuracy Recovery on Deep Learning Hardware
Existing methods to recover model accuracy on analog-digital hardware in the presence of quantization and analog noise include noise-injection training.
Adaptive Block Floating-Point for Analog Deep Learning Hardware
Analog mixed-signal (AMS) devices promise faster, more energy-efficient deep neural network (DNN) inference than their digital counterparts.
