Search Results for author:
Found 14 papers, 5 papers with code
Speculative Decoding for Verilog: Speed and Quality, All in One
The rapid advancement of large language models (LLMs) has revolutionized code generation tasks across various programming languages.
ROMA: a Read-Only-Memory-based Accelerator for QLoRA-based On-Device LLM
In this work, we propose ROMA, a QLoRA accelerator with a hybrid storage architecture that uses ROM for quantized base models and SRAM for LoRA weights and KV cache.
Finetuning Generative Trajectory Model with Reinforcement Learning from Human Feedback
To address this, we introduce TrajHF, a human feedback-driven finetuning framework for generative trajectory models, designed to align motion planning with diverse driving preferences.
DeepGate4: Efficient and Effective Representation Learning for Circuit Design at Scale
Circuit representation learning has become pivotal in electronic design automation, enabling critical tasks such as testability analysis, logic reasoning, power estimation, and SAT solving.
Automating Energy-Efficient GPU Kernel Generation: A Fast Search-Based Compilation Approach
Furthermore, we introduce a dynamic updating strategy for the energy cost model, reducing the need for on-device energy measurements and accelerating the search process.
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.
Enhancing Vectorized Map Perception with Historical Rasterized Maps
To fully exploit a historical map, we propose two novel modules to enhance BEV features and map element queries.
Leveraging Enhanced Queries of Point Sets for Vectorized Map Construction
In autonomous driving, the high-definition (HD) map plays a crucial role in localization and planning.
BitDistiller: Unleashing the Potential of Sub-4-Bit LLMs via Self-Distillation
The upscaling of Large Language Models (LLMs) has yielded impressive advances in natural language processing, yet it also poses significant deployment challenges.
AFPQ: Asymmetric Floating Point Quantization for LLMs
Large language models (LLMs) show great performance in various tasks, but face deployment challenges from limited memory capacity and bandwidth.
Large Trajectory Models are Scalable Motion Predictors and Planners
STR reformulates the motion prediction and motion planning problems by arranging observations, states, and actions into one unified sequence modeling task.
Adam Accumulation to Reduce Memory Footprints of both Activations and Gradients for Large-scale DNN Training
We find that previous gradient accumulation reduces activation memory but fails to be compatible with gradient memory reduction due to a contradiction between preserving gradients and releasing gradients.
Integer or Floating Point? New Outlooks for Low-Bit Quantization on Large Language Models
In this study, we conduct a comparative analysis of INT and FP quantization with the same bit-width, revealing that the optimal quantization format varies across different layers due to the complexity and diversity of tensor distribution.
Parallel Inference for Latent Dirichlet Allocation on Graphics Processing Units
Extensive experiments showed that our parallel inference methods consistently produced LDA models with the same predictive power as sequential training methods did but with 26x speedup for CGS and 196x speedup for CVB on a GPU with 30 multiprocessors; actually the speedup is almost linearly scalable with the number of multiprocessors available.
