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Found 23 papers, 9 papers with code
AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment
Motivated by the transformative capabilities of large language models (LLMs) across various natural language tasks, there has been a growing demand to deploy these models effectively across diverse real-world applications and platforms.
Model Tells You Where to Merge: Adaptive KV Cache Merging for LLMs on Long-Context Tasks
To mitigate computational costs, LLMs often employ the KV Cache technique to improve the generation speed.
MG-Verilog: Multi-grained Dataset Towards Enhanced LLM-assisted Verilog Generation
Furthermore, to fully exploit the potential of the MG-Verilog dataset, which varies in complexity and detail, we introduce a balanced fine-tuning scheme.
Unveiling and Harnessing Hidden Attention Sinks: Enhancing Large Language Models without Training through Attention Calibration
Specifically, this work begins with comprehensive visualizations of the attention distributions in LLMs during inference across various inputs and tasks.
EDGE-LLM: Enabling Efficient Large Language Model Adaptation on Edge Devices via Layerwise Unified Compression and Adaptive Layer Tuning and Voting
Efficient adaption of large language models (LLMs) on edge devices is essential for applications requiring continuous and privacy-preserving adaptation and inference.
GPT4AIGChip: Towards Next-Generation AI Accelerator Design Automation via Large Language Models
To our knowledge, this work is the first to demonstrate an effective pipeline for LLM-powered automated AI accelerator generation.
Master-ASR: Achieving Multilingual Scalability and Low-Resource Adaptation in ASR with Modular Learning
Despite the impressive performance recently achieved by automatic speech recognition (ASR), we observe two primary challenges that hinder its broader applications: (1) The difficulty of introducing scalability into the model to support more languages with limited training, inference, and storage overhead; (2) The low-resource adaptation ability that enables effective low-resource adaptation while avoiding over-fitting and catastrophic forgetting issues.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
NetBooster: Empowering Tiny Deep Learning By Standing on the Shoulders of Deep Giants
Tiny deep learning has attracted increasing attention driven by the substantial demand for deploying deep learning on numerous intelligent Internet-of-Things devices.
Hint-Aug: Drawing Hints from Foundation Vision Transformers Towards Boosted Few-Shot Parameter-Efficient Tuning
To tackle this challenge, we first identify an opportunity for FViTs in few-shot tuning: pretrained FViTs themselves have already learned highly representative features from large-scale pretraining data, which are fully preserved during widely used parameter-efficient tuning.
Losses Can Be Blessings: Routing Self-Supervised Speech Representations Towards Efficient Multilingual and Multitask Speech Processing
We believe S$^3$-Router has provided a new perspective for practical deployment of speech SSL models.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
ViTCoD: Vision Transformer Acceleration via Dedicated Algorithm and Accelerator Co-Design
Specifically, on the algorithm level, ViTCoD prunes and polarizes the attention maps to have either denser or sparser fixed patterns for regularizing two levels of workloads without hurting the accuracy, largely reducing the attention computations while leaving room for alleviating the remaining dominant data movements; on top of that, we further integrate a lightweight and learnable auto-encoder module to enable trading the dominant high-cost data movements for lower-cost computations.
LDP: Learnable Dynamic Precision for Efficient Deep Neural Network Training and Inference
While existing works mostly fix the model precision during the whole training process, a few pioneering works have shown that dynamic precision schedules help DNNs converge to a better accuracy while leading to a lower training cost than their static precision training counterparts.
MIA-Former: Efficient and Robust Vision Transformers via Multi-grained Input-Adaptation
ViTs are often too computationally expensive to be fitted onto real-world resource-constrained devices, due to (1) their quadratically increased complexity with the number of input tokens and (2) their overparameterized self-attention heads and model depth.
Identification of Pediatric Respiratory Diseases Using Fine-grained Diagnosis System
The first stage structuralizes test results by extracting relevant numerical values from clinical notes, and the disease identification stage provides a diagnosis based on text-form clinical notes and the structured data obtained from the first stage.
O-HAS: Optical Hardware Accelerator Search for Boosting Both Acceleration Performance and Development Speed
The recent breakthroughs and prohibitive complexities of Deep Neural Networks (DNNs) have excited extensive interest in domain-specific DNN accelerators, among which optical DNN accelerators are particularly promising thanks to their unprecedented potential of achieving superior performance-per-watt.
A3C-S: Automated Agent Accelerator Co-Search towards Efficient Deep Reinforcement Learning
Driven by the explosive interest in applying deep reinforcement learning (DRL) agents to numerous real-time control and decision-making applications, there has been a growing demand to deploy DRL agents to empower daily-life intelligent devices, while the prohibitive complexity of DRL stands at odds with limited on-device resources.
InstantNet: Automated Generation and Deployment of Instantaneously Switchable-Precision Networks
The promise of Deep Neural Network (DNN) powered Internet of Thing (IoT) devices has motivated a tremendous demand for automated solutions to enable fast development and deployment of efficient (1) DNNs equipped with instantaneous accuracy-efficiency trade-off capability to accommodate the time-varying resources at IoT devices and (2) dataflows to optimize DNNs' execution efficiency on different devices.
HW-NAS-Bench:Hardware-Aware Neural Architecture Search Benchmark
To design HW-NAS-Bench, we carefully collected the measured/estimated hardware performance of all the networks in the search spaces of both NAS-Bench-201 and FBNet, on six hardware devices that fall into three categories (i. e., commercial edge devices, FPGA, and ASIC).
Hardware Aware Neural Architecture Search
Neural Architecture Search
HW-NAS-Bench: Hardware-Aware Neural Architecture Search Benchmark
To design HW-NAS-Bench, we carefully collected the measured/estimated hardware performance (e. g., energy cost and latency) of all the networks in the search space of both NAS-Bench-201 and FBNet, considering six hardware devices that fall into three categories (i. e., commercial edge devices, FPGA, and ASIC).
Hardware Aware Neural Architecture Search
Neural Architecture Search
Auto-Agent-Distiller: Towards Efficient Deep Reinforcement Learning Agents via Neural Architecture Search
We therefore propose an Auto-Agent-Distiller (A2D) framework, which to our best knowledge is the first neural architecture search (NAS) applied to DRL to automatically search for the optimal DRL agents for various tasks that optimize both the test scores and efficiency.
Kernel Quantization for Efficient Network Compression
Thus, KQ can represent the weight tensor in the convolution layer with low-bit indexes and a kernel codebook with limited size, which enables KQ to achieve significant compression ratio.
Exploiting Partially Annotated Data in Temporal Relation Extraction
As a result, only a small number of documents are typically annotated, limiting the coverage of various lexical/semantic phenomena.
Exploiting Partially Annotated Data for Temporal Relation Extraction
As a result, only a small number of documents are typically annotated, limiting the coverage of various lexical/semantic phenomena.
