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Found 14 papers, 9 papers with code
APOLLO: SGD-like Memory, AdamW-level Performance
This memory burden necessitates using more or higher-end GPUs or reducing batch sizes, limiting training scalability and throughput.
PACE: Pacing Operator Learning to Accurate Optical Field Simulation for Complicated Photonic Devices
In this work, we boost the prediction fidelity to an unprecedented level for simulating complex photonic devices with a novel operator design driven by the above challenges.
INSIGHT: Universal Neural Simulator for Analog Circuits Harnessing Autoregressive Transformers
However, efficient and effective exploration of the vast and complex design space remains constrained by the time-consuming nature of SPICE simulations, making effective design automation a challenging endeavor.
LLM-Enhanced Bayesian Optimization for Efficient Analog Layout Constraint Generation
Analog layout synthesis faces significant challenges due to its dependence on manual processes, considerable time requirements, and performance instability.
M3ICRO: Machine Learning-Enabled Compact Photonic Tensor Core based on PRogrammable Multi-Operand Multimode Interference
The programmable MOMMI leverages the intrinsic light propagation principle, providing a single-device programmable matrix unit beyond the conventional computing paradigm of one multiply-accumulate (MAC) operation per device.
Integrated multi-operand optical neurons for scalable and hardware-efficient deep learning
The optical neural network (ONN) is a promising hardware platform for next-generation neuromorphic computing due to its high parallelism, low latency, and low energy consumption.
Pre-RMSNorm and Pre-CRMSNorm Transformers: Equivalent and Efficient Pre-LN Transformers
Experiments demonstrate that we can reduce the training and inference time of Pre-LN Transformers by 1% - 10%.
NeurOLight: A Physics-Agnostic Neural Operator Enabling Parametric Photonic Device Simulation
In this work, for the first time, a physics-agnostic neural operator-based framework, dubbed NeurOLight, is proposed to learn a family of frequency-domain Maxwell PDEs for ultra-fast parametric photonic device simulation.
Uni-Retriever: Towards Learning The Unified Embedding Based Retriever in Bing Sponsored Search
On the other hand, the capability of making high-CTR retrieval is optimized by learning to discriminate user's clicked ads from the entire corpus.
ELight: Enabling Efficient Photonic In-Memory Neurocomputing with Life Enhancement
With the recent advances in optical phase change material (PCM), photonic in-memory neurocomputing has demonstrated its superiority in optical neural network (ONN) designs with near-zero static power consumption, time-of-light latency, and compact footprint.
A compact butterfly-style silicon photonic-electronic neural chip for hardware-efficient deep learning
The optical neural network (ONN) is a promising hardware platform for next-generation neurocomputing due to its high parallelism, low latency, and low energy consumption.
L2ight: Enabling On-Chip Learning for Optical Neural Networks via Efficient in-situ Subspace Optimization
In this work, we propose a closed-loop ONN on-chip learning framework L2ight to enable scalable ONN mapping and efficient in-situ learning.
Towards Memory-Efficient Neural Networks via Multi-Level in situ Generation
Deep neural networks (DNN) have shown superior performance in a variety of tasks.
