Search Results for author:
Found 12 papers, 6 papers with code
An Efficient Training Framework for Reversible Neural Architectures
In this work, we formulate the decision problem for reversible operators with training time as the objective function and memory usage as the constraint.
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.
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%.
TripLe: Revisiting Pretrained Model Reuse and Progressive Learning for Efficient Vision Transformer Scaling and Searching
One promising way to accelerate transformer training is to reuse small pretrained models to initialize the transformer, as their existing representation power facilitates faster model convergence.
PC-SNN: Supervised Learning with Local Hebbian Synaptic Plasticity based on Predictive Coding in Spiking Neural Networks
Deemed as the third generation of neural networks, the event-driven Spiking Neural Networks(SNNs) combined with bio-plausible local learning rules make it promising to build low-power, neuromorphic hardware for SNNs.
Delving into Effective Gradient Matching for Dataset Condensation
In this work, we delve into the gradient matching method from a comprehensive perspective and answer the critical questions of what, how, and where to match.
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.
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.
Delving into Macro Placement with Reinforcement Learning
In physical design, human designers typically place macros via trial and error, which is a Markov decision process.
Towards Memory-Efficient Neural Networks via Multi-Level in situ Generation
Deep neural networks (DNN) have shown superior performance in a variety of tasks.
Optimizer Fusion: Efficient Training with Better Locality and Parallelism
Machine learning frameworks adopt iterative optimizers to train neural networks.
Logic Synthesis Meets Machine Learning: Trading Exactness for Generalization
If the function is incompletely-specified, the implementation has to be true only on the care set.
