Search Results for author:
Found 13 papers, 1 papers with code
Large Language Model Inference Acceleration: A Comprehensive Hardware Perspective
We compare the performance of the same optimization methods across different hardware platforms, the performance across different hardware platforms, and the performance of different methods on the same hardware platform.
MARCA: Mamba Accelerator with ReConfigurable Architecture
We propose intra-operation buffer management strategy to maximize input data sharing for linear operations within operations, and inter-operation strategy for element-wise operations between operations.
Visual-Text Cross Alignment: Refining the Similarity Score in Vision-Language Models
The local visual areas are then cross-aligned with the finer descriptions by creating a similarity matrix using the pre-trained VLM.
D2E-An Autonomous Decision-making Dataset involving Driver States and Human Evaluation
Although current datasets have made significant progress in the collection of vehicle and environment data, emphasis on human-end data including the driver states and human evaluation is not sufficient.
Temporal-Aware Deep Reinforcement Learning for Energy Storage Bidding in Energy and Contingency Reserve Markets
To bridge this gap, we develop a novel BESS joint bidding strategy that utilizes deep reinforcement learning (DRL) to bid in the spot and contingency frequency control ancillary services (FCAS) markets.
Deep Reinforcement Learning for Voltage Control and Renewable Accommodation Using Spatial-Temporal Graph Information
Renewable energy resources (RERs) have been increasingly integrated into distribution networks (DNs) for decarbonization.
Attentive Convolutional Deep Reinforcement Learning for Optimizing Solar-Storage Systems in Real-Time Electricity Markets
This paper studies the synergy of solar-battery energy storage system (BESS) and develops a viable strategy for the BESS to unlock its economic potential by serving as a backup to reduce solar curtailments while also participating in the electricity market.
Fast and Efficient 2-bit LLM Inference on GPU: 2/4/16-bit in a Weight Matrix with Asynchronous Dequantization
To tackle these challenges and enable fast and efficient LLM inference on GPUs, we propose the following techniques in this paper.
Cross-Entropy-Based Approach to Multi-Objective Electric Vehicle Charging Infrastructure Planning
Therefore, we are motivated to develop a comprehensive multi-objective framework for optimal CS placement in a traffic network overlaid by a distribution network, considering multiple stakeholders' interested factors, such as traffic flow, PEV charging time cost, PEV travel distance, and the reliability of the distribution network.
Optimal Energy Storage Scheduling for Wind Curtailment Reduction and Energy Arbitrage: A Deep Reinforcement Learning Approach
However, the variable nature of wind generation can undermine system reliability and lead to wind curtailment, causing substantial economic losses to wind power producers.
Deep Reinforcement Learning for Wind and Energy Storage Coordination in Wholesale Energy and Ancillary Service Markets
Simulations also show that using curtailed wind generation as a power source for charging the BESS can lead to additional financial gains.
Model-Free Approach to Fair Solar PV Curtailment Using Reinforcement Learning
This paper investigates reinforcement learning, which gradually optimizes a fair PV curtailment strategy by interacting with the system.
Deep Reinforcement Learning for Optimal Power Flow with Renewables Using Graph Information
Considering uncertainties and voltage fluctuation issues introduced by RERs, in this paper, we propose a deep reinforcement learning (DRL)-based strategy leveraging spatial-temporal (ST) graphical information of power systems, to dynamically search for the optimal operation, i. e., optimal power flow (OPF), of power systems with a high uptake of RERs.
