SOTAVerified

Computational Efficiency

Methods and optimizations to reduce the computational resources (e.g., time, memory, or power) needed for training and inference in models. This involves techniques that streamline processing, optimize algorithms, or leverage hardware to enhance performance without compromising accuracy.

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2831–2840 of 4891 papers

TitleStatusHype
Speech to Text Adaptation: Towards an Efficient Cross-Modal Distillation—0
Speeding up Local Optimization in Vehicle Routing with Tensor-based GPU Acceleration—0
SPEQ: Stabilization Phases for Efficient Q-Learning in High Update-To-Data Ratio Reinforcement Learning—0
Spherical U-Net on Cortical Surfaces: Methods and Applications—0
SpiDR: A Reconfigurable Digital Compute-in-Memory Spiking Neural Network Accelerator for Event-based Perception—0
Spike-Kal: A Spiking Neuron Network Assisted Kalman Filter—0
SpikePack: Enhanced Information Flow in Spiking Neural Networks with High Hardware Compatibility—0
SpikeSift: A Computationally Efficient and Drift-Resilient Spike Sorting Algorithm—0
SPikE-SSM: A Sparse, Precise, and Efficient Spiking State Space Model for Long Sequences Learning—0
SpINR: Neural Volumetric Reconstruction for FMCW Radars—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified