Research and Materials on Hardware implementation of Transformer Models
You can add new papers via pull requests, Please check data/papers.yaml and if your paper is not in list, add entity at the last item and create pull request.
-
BERT is a method of pre-training language representations, meaning that we train a general-purpose language understanding model on a large text corpus (like Wikipedia) and then use that model for downstream NLP tasks.
-
BERT was created and published in 2018 by Jacob Devlin and his colleagues from Google. BERT is conceptually simple and empirically powerful. It obtains new state-of-the-art results on eleven natural language processing tasks.
- BERT is a Transformer-based model.
- The architecture of BERT is similar to the original Transformer model, except that BERT has two separate Transformer models: one for the left-to-right direction (the “encoder”) and one for the right-to-left direction (the “encoder”).
- The output of each model is the hidden state output by the final Transformer layer. The two models are pre-trained jointly on a large corpus of unlabeled text. The pre-training task is a simple and straightforward masked language modeling objective.
- The pre-trained BERT model can then be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inference, without substantial task-specific architecture modifications.
1. Attention Is All You Need
2. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
BERT is conceptually simple and empirically powerful. It obtains new state-of-the-art results on eleven natural language processing tasks, including pushing the GLUE score to 80.5% (7.7% point absolute improvement), MultiNLI accuracy to 86.7% (4.6% absolute improvement), SQuAD v1.1 question answering Test F1 to 93.2 (1.5 point absolute improvement) and SQuAD v2.0 Test F1 to 83.1 (5.1 point absolute improvement).
Algorithm-Hardware Co-Design of Single Shot Detector for Fast Object Detection on FPGAs
SparseNN: An energy-efficient neural network accelerator exploiting input and output sparsity
A Power Efficient Neural Network Implementation on Heterogeneous FPGA and GPU Devices
A Simple and Effective Approach to Automatic Post-Editing with Transfer Learning
An Evaluation of Transfer Learning for Classifying Sales Engagement Emails at Large Scale
MAGNet: A Modular Accelerator Generator for Neural Networks
mRNA: Enabling Efficient Mapping Space Exploration for a Reconfiguration Neural Accelerator
Pre-trained bert-gru model for relation extraction
Q8BERT: Quantized 8Bit BERT
Structured pruning of a BERT-based question answering model
Structured pruning of large language models
Tinybert: Distilling bert for natural language understanding
A Low-Cost Reconfigurable Nonlinear Core for Embedded DNN Applications
A Multi-Neural Network Acceleration Architecture
A Primer in BERTology: What We Know About How BERT Works
A Reconfigurable DNN Training Accelerator on FPGA
A^3: Accelerating Attention Mechanisms in Neural Networks with Approximation
Emerging Neural Workloads and Their Impact on Hardware
Accelerating event detection with DGCNN and FPGAS
An Empirical Analysis of BERT Embedding for Automated Essay Scoring
An investigation on different underlying quantization schemes for pre-trained language models
ATT: A Fault-Tolerant ReRAM Accelerator for Attention-based Neural Networks
Binarybert: Pushing the limit of bert quantization
Capuchin: Tensor-based GPU Memory Management for Deep Learning
CATBERT: Context-Aware Tiny BERT for Detecting Social Engineering Emails
CatBERT: Context-Aware Tiny BERT for Detecting Targeted Social Engineering Emails
ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT
Combining Feature Selection Methods with BERT: An In-depth Experimental Study of Long Text Classification
Comparison of Deep Learning Models and Various Text Pre-Processing Techniques for the Toxic Comments Classification
Compressing BERT: Studying the Effects of Weight Pruning on Transfer Learning
Compression of deep learning models for NLP
Deep Learning Acceleration with Neuron-to-Memory Transformation
Earlybert: Efficient bert training via early-bird lottery tickets
Efficient algorithms and hardware for natural language processing
Efficient transformer-based large scale language representations using hardware-friendly block structured pruning
FARM: A flexible accelerator for recurrent and memory augmented neural networks
Fastformers: Highly efficient transformer models for natural language understanding
FTRANS: energy-efficient acceleration of transformers using FPGA
Hardware accelerator for multi-head attention and position-wise feed-forward in the transformer
Improving Accuracy and Speeding Up Document Image Classification Through Parallel Systems
Improving post training neural quantization: Layer-wise calibration and integer programming
Integer quantization for deep learning inference: Principles and empirical evaluation
Ladabert: Lightweight adaptation of bert through hybrid model compression
Load What You Need: Smaller Versions of Multilingual BERT
Look-Up Table based Energy Efficient Processing in Cache Support for Neural Network Acceleration
MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers
Movement Pruning: Adaptive Sparsity by Fine-Tuning
MSP: an FPGA-specific mixed-scheme, multi-precision deep neural network quantization framework
Poor Man's BERT: Smaller and Faster Transformer Models
PoWER-BERT: Accelerating BERT Inference via Progressive Word-vector Elimination
Pruning Redundant Mappings in Transformer Models via Spectral-Normalized Identity Prior
Q-BERT: Hessian Based Ultra Low Precision Quantization of BERT
ReTransformer: ReRAM-based processing-in-memory architecture for transformer acceleration
SqueezeBERT: What can computer vision teach NLP about efficient neural networks?
TernaryBERT: Distillation-aware Ultra-low Bit BERT
Training Large Neural Networks with Constant Memory using a New Execution Algorithm
Ultron-AutoML: An open-source, distributed, scalable framework for efficient hyper-parameter optimization
Towards Fully 8-bit Integer Inference for the Transformer Model
TopicBERT for energy efficient document classification
A Framework for Area-efficient Multi-task BERT Execution on ReRAM-based Accelerators
A Full-Stack Search Technique for Domain Optimized Deep Learning Accelerators
A Microcontroller is All You Need: Enabling Transformer Execution on Low-Power IoT Endnodes
A Quantitative Survey of Communication Optimizations in Distributed Deep Learning
A Study on Token Pruning for ColBERT
A White Paper on Neural Network Quantization
Accelerated Device Placement Optimization with Contrastive Learning
Accelerating bandwidth-bound deep learning inference with main-memory accelerators
Accelerating Emerging Neural Workloads
Accelerating Framework of Transformer by Hardware Design and Model Compression Co-Optimization
Accelerating Transformer-based Deep Learning Models on FPGAs using Column Balanced Block Pruning
Accommodating Transformer onto FPGA: Coupling the Balanced Model Compression and FPGA-Implementation Optimization
Adapting by pruning: A case study on BERT
Adaptive Inference through Early-Exit Networks: Design, Challenges and Directions
Adaptive Spatio-Temporal Graph Enhanced Vision-Language Representation for Video QA
Algorithm-hardware Co-design of Attention Mechanism on FPGA Devices
Aquabolt-XL: Samsung HBM2-PIM with in-memory processing for ML accelerators and beyond
AUBER: Automated BERT regularization
Automatic Mixed-Precision Quantization Search of BERT
BERMo: What can BERT learn from ELMo?
BERT Model for Classification of Fake News using the Cloud Processing Capacity
Bertinho: Galician BERT representations
BERxiT: Early exiting for BERT with better fine-tuning and extension to regression
Beyond preserved accuracy: Evaluating loyalty and robustness of BERT compression
Binary Complex Neural Network Acceleration on FPGA : (Invited Paper)
Biomedical Named Entity Recognition at Scale
Block pruning for faster transformers
Compressing Large-Scale Transformer-Based Models: A Case Study on BERT
DAP-BERT: Differentiable Architecture Pruning of BERT
Demystifying BERT: Implications for Accelerator Design
Dynamic-TinyBERT: Boost TinyBERT's Inference Efficiency by Dynamic Sequence Length
EAGLE: Expedited Device Placement with Automatic Grouping for Large Models
EBERT: Efficient BERT Inference with Dynamic Structured Pruning
EdgeBERT: Sentence-Level Energy Optimizations for Latency-Aware Multi-Task NLP Inference
ELSA: Hardware-Software co-design for efficient, lightweight self-attention mechanism in neural networks
Enabling energy-efficient DNN training on hybrid GPU-FPGA accelerators
Enabling One-Size-Fits-All Compilation Optimization for Inference Across Machine Learning Computers
Energy efficiency boost in the AI-infused POWER10 processor
Fixed-point Quantization for Vision Transformer
FlexACC: A Programmable Accelerator with Application-Specific ISA for Flexible Deep Neural Network Inference
Gemmini: Enabling systematic deep-learning architecture evaluation via full-stack integration
Gobo: Quantizing attention-based nlp models for low latency and energy efficient inference
Hardware Acceleration of Fully Quantized BERT for Efficient Natural Language Processing
Hardware acceleration of sparse and irregular tensor computations of ml models: A survey and insights
HMC-TRAN: A Tensor-core Inspired Hierarchical Model Compression for Transformer-based DNNs on GPU
HoloFormer: Deep Compression of Pre-Trained Transforms via Unified Optimization of N: M Sparsity and Integer Quantization
How Deep Learning Model Architecture and Software Stack Impacts Training Performance in the Cloud
How to Train BERT with an Academic Budget
I-BERT: Integer-only BERT Quantization
Improving the efficiency of transformers for resource-constrained devices
KAISA: An adaptive second-order optimizer framework for deep neural networks
KDLSQ-BERT: A Quantized Bert Combining Knowledge Distillation with Learned Step Size Quantization
Kunlun: A 14nm High-Performance AI Processor for Diversified Workloads
Layerweaver: Maximizing Resource Utilization of Neural Processing Units via Layer-Wise Scheduling
Learning Light-Weight Translation Models from Deep Transformer
M2M: Learning to Enhance Low-Light Image from Model to Mobile FPGA
NAS-BERT: Task-Agnostic and Adaptive-Size BERT Compression with Neural Architecture Search
NeuralScale: A RISC-V Based Neural Processor Boosting AI Inference in Clouds
NLP-Fast: A Fast, Scalable, and Flexible System to Accelerate Large-Scale Heterogeneous NLP Models
NPE: An FPGA-based Overlay Processor for Natural Language Processing
Predicting Efficiency/Effectiveness Trade-offs for Dense vs. Sparse Retrieval Strategy Selection
PTQ4ViT: Post-Training Quantization Framework for Vision Transformers with Twin Uniform Quantization
Randomly Wired Network Based on RoBERTa and Dialog History Attention for Response Selection
Re2PIM: A Reconfigurable ReRAM-Based PIM Design for Variable-Sized Vector-Matrix Multiplication
RISC-VTF: RISC-V Based Extended Instruction Set for Transformer
RMSMP: A Novel Deep Neural Network Quantization Framework with Row-wise Mixed Schemes and Multiple Precisions
Sanger: A Co-Design Framework for Enabling Sparse Attention using Reconfigurable Architecture
Simplified TinyBERT: Knowledge Distillation for Document Retrieval
SmaQ: Smart Quantization for DNN Training by Exploiting Value Clustering
Softermax: Hardware/Software Co-Design of an Efficient Softmax for Transformers
SpAtten: Efficient Sparse Attention Architecture with Cascade Token and Head Pruning
SQuAT: Sharpness- and Quantization-Aware Training for BERT
Stochastic precision ensemble: self-knowledge distillation for quantized deep neural networks
Talos: A Weighted Speedup-Aware Device Placement of Deep Learning Models
TR-BERT: Dynamic Token Reduction for Accelerating BERT Inference
Training with Quantization Noise for Extreme Model Compression
Transformer Acceleration with Dynamic Sparse Attention
Understanding and Overcoming the Challenges of Efficient Transformer Quantization
Vis-TOP: Visual Transformer Overlay Processor
Elbert: Fast albert with confidence-window based early exit
Ghostbert: Generate more features with cheap operations for BERT
ProtTrans: Toward Understanding the Language of Life Through Self-Supervised Learning
Prune once for all: Sparse pre-trained language models
ROSITA: Refined BERT cOmpreSsion with InTegrAted techniques
VS-Quant: Per-vector Scaled Quantization for Accurate Low-Precision Neural Network Inference
A 28nm 27.5TOPS/W Approximate-Computing-Based Transformer Processor with Asymptotic Sparsity Speculating and Out-of-Order Computing
A 40nm 5.6TOPS/W 239GOPS/mm2 Self-Attention Processor with Sign Random Projection-based Approximation
A Dual-Mode Similarity Search Accelerator based on Embedding Compression for Online Cross-Modal Image-Text Retrieval
A Fast and Flexible FPGA-based Accelerator for Natural Language Processing Neural Networks
A Fast Post-Training Pruning Framework for Transformers
A Framework for Accelerating Transformer-Based Language Model on ReRAM-Based Architecture
A length adaptive algorithm-hardware co-design of transformer on FPGA through sparse attention and dynamic pipelining
A Lite Romanian BERT: ALR-BERT
A Resource-Saving Energy-Efficient Reconfigurable Hardware Accelerator for BERT-based Deep Neural Network Language Models using FFT Multiplication
A Self-Attention Network for Deep JSCCM: The Design and FPGA Implementation
Accelerating attention mechanism on fpgas based on efficient reconfigurable systolic array
Accelerating attention through gradient-based learned runtime pruning
Accelerating NLP Tasks on FPGA with Compressed BERT and a Hardware-Oriented Early Exit Method
Accelerating Transformer Networks through Recomposing Softmax Layers
Achieving the Performance of All-Bank In-DRAM PIM With Standard Memory Interface: Memory-Computation Decoupling
Adaptable Butterfly Accelerator for Attention-based NNs via Hardware and Algorithm Co-design
AlphaTuning: Quantization-Aware Parameter-Efficient Adaptation of Large-Scale Pre-Trained Language Models
Alternative non-BERT model choices for the textual classification in low-resource languages and environments
An Algorithm-Hardware Co-Optimized Framework for Accelerating N:M Sparse Transformers
An Automatic and Efficient BERT Pruning for Edge AI Systems
An Efficient Hardware Accelerator for Sparse Transformer Neural Networks
An Energy-Efficient Transformer Processor Exploiting Dynamic Weak Relevances in Global Attention
An FPGA-Based Transformer Accelerator Using Output Block Stationary Dataflow for Object Recognition Applications
Analog-memory-based 14nm Hardware Accelerator for Dense Deep Neural Networks including Transformers
Answer Fast: Accelerating BERT on the Tensor Streaming Processor
ANT: Exploiting Adaptive Numerical Data Type for Low-bit Deep Neural Network Quantization
APT: The master-copy-free training method for quantised neural network on edge devices
Auto-ViT-Acc: An FPGA-Aware Automatic Acceleration Framework for Vision Transformer with Mixed-Scheme Quantization
Balance Multi-Head Attention based on Software and Hardware Co-design
BEBERT: Efficient and robust binary ensemble BERT
BERT model optimization methods for inference: a comparative study of five alternative BERT-model implementations
BERT on a Data Diet: Finding Important Examples by Gradient-Based Pruning
BERTPerf: Inference Latency Predictor for BERT on ARM big.LITTLE Multi-Core Processors
BiBERT: Accurate Fully Binarized BERT
Bigger&Faster: Two-stage Neural Architecture Search for Quantized Transformer Models
BiT: Robustly Binarized Multi-distilled Transformer
Boosting Distributed Training Performance of the Unpadded BERT Model
Compact Token Representations with Contextual Quantization for Efficient Document Re-ranking
Compressing Pre-trained Transformers via Low-Bit NxM Sparsity for Natural Language Understanding
Compression of Generative Pre-trained Language Models via Quantization
CONNA: Configurable Matrix Multiplication Engine for Neural Network Acceleration
CPSAA: Accelerating Sparse Attention using Crossbar-based Processing-In-Memory Architecture
Demystifying BERT: System Design Implications
DFX: A Low-latency Multi-FPGA Appliance for Accelerating Transformer-based Text Generation
DiVIT: Algorithm and architecture co-design of differential attention in vision transformer
DOTA: Detect and Omit Weak Attentions for Scalable Transformer Acceleration
DQ-BART: Efficient Sequence-to-Sequence Model via Joint Distillation and Quantization
DTQAtten: Leveraging Dynamic Token-based Quantization for Efficient Attention Architecture
Dynamic Precision Analog Computing for Neural Networks
EFA-Trans: An Efficient and Flexible Acceleration Architecture for Transformers
Efficient Document Retrieval by End-to-End Refining and Quantizing BERT Embedding with Contrastive Product Quantization
Elastic Processing and Hardware Architectures for Machine Learning
Empirical Evaluation of Post-Training Quantization Methods for Language Tasks
Enabling and Accelerating Dynamic Vision Transformer Inference for Real-Time Applications
Enabling Efficient Large-Scale Deep Learning Training with Cache Coherent Disaggregated Memory Systems
Enabling Energy-Efficient Inference for Self-Attention Mechanisms in Neural Networks
Enabling fast uncertainty estimation: accelerating bayesian transformers via algorithmic and hardware optimizations
Enabling Fast Uncertainty Estimation: Exploiting Structured Sparsity in Bayesian Transformers
Ensemble Model Compression for Fast and Energy-Efficient Ranking on FPGAs
Extending the ONNX Runtime Framework for the Processing-in-Memory Execution
Extreme Compression for Pre-trained Transformers Made Simple and Efficient
Fast Heterogeneous Task Mapping for Reducing Edge DNN Latency
FILM-QNN: Efficient FPGA Acceleration of Deep Neural Networks with Intra-Layer, Mixed-Precision Quantization
Fine-and Coarse-Granularity Hybrid Self-Attention for Efficient BERT
FPGA-aware automatic acceleration framework for vision transformer with mixed-scheme quantization: late breaking results
FPGA-based design and implementation of the location attention mechanism in neural networks
From dense to sparse: Contrastive pruning for better pre-trained language model compression
Future Scaling of Memory Hierarchy for Tensor Cores and Eliminating Redundant Shared Memory Traffic Using Inter-Warp Multicastin
Greedy-layer pruning: Speeding up transformer models for natural language processing
GuardNN: secure accelerator architecture for privacy-preserving deep learning
Handling heavy-tailed input of transformer inference on GPUs
Hardware Acceleration of Transformer Networks using FPGAs
Hardware and Software Co-design for Soft Switch in ViT Variants Processing Unit
Hardware and Software Co-optimization for Windows Attention
Improving Oversubscribed GPU Memory Performance in the PyTorch Framework
Integer Fine-tuning of Transformer-based Models
Learned Token Pruning in Contextualized Late Interaction over BERT (ColBERT)
Lightweight Composite Re-Ranking for Efficient Keyword Search with BERT
Lightweight Transformers for Conversational AI
LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale
Low-Bit Quantization of Transformer for Audio Speech Recognition
Low-Precision Quantization Techniques for Hardware-Implementation-Friendly BERT Models
MKQ-BERT: Quantized BERT with 4-bits Weights and Activations
Mokey: enabling narrow fixed-point inference for out-of-the-box floating-point transformer models
Mr. BiQ: Post-Training Non-Uniform Quantization Based on Minimizing the Reconstruction Error
Near-Optimal Sparse Allreduce for Distributed Deep Learning
Nebula: A Scalable and Flexible Accelerator for DNN Multi-Branch Blocks on Embedded Systems
NEEBS: Nonexpert large-scale environment building system for deep neural network
Optimal Brain Compression: A framework for accurate post-training quantization and pruning
PipeBERT: High-throughput BERT Inference for ARM Big.LITTLE Multi-core Processors
Post-Training Quantization for Longformer with Chunkwise Quantization Granularity and Optimized Percentile
Pre-trained Language Model with Feature Reduction and No Fine-Tuning
Privacy-Preserving Text Classification on BERT Embeddings with Homomorphic Encryption
ProSE: the architecture and design of a protein discovery engine
QDrop: Randomly Dropping Quantization for Extremely Low-bit Post-Training Quantization
QuaLA-MiniLM: a Quantized Length Adaptive MiniLM
RCT: Resource Constrained Training for Edge AI
ReAAP: A Reconfigurable and Algorithm-Oriented Array Processor With Compiler-Architecture Co-Design
Row-wise Accelerator for Vision Transformer
S4: a High-sparsity, High-performance AI Accelerator
SALO: an efficient spatial accelerator enabling hybrid sparse attention mechanisms for long sequences
Searching for memory-lighter architectures for OCR-augmented image captioning
SensiMix: Sensitivity-Aware 8-bit index & 1-bit value mixed precision quantization for BERT compression
Sentiment Analysis Using Pre-Trained Language Model With No Fine-Tuning and Less Resource
Software and Hardware Fusion Multi-Head Attention
Sparse Attention Acceleration with Synergistic In-Memory Pruning and On-Chip Recomputation
SwiftPruner: Reinforced Evolutionary Pruning for Efficient Ad Relevance
T-OPU: An FPGA-based Overlay Processor for Natural Language Processing
The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models
Towards efficient post-training quantization of pre-trained language models
Train Flat, Then Compress: Sharpness-Aware Minimization Learns More Compressible Models
TranCIM: Full-Digital Bitline-Transpose CIM-based Sparse Transformer Accelerator With Pipeline/Parallel Reconfigurable Modes
TransPIM: A Memory-based Acceleration via Software-Hardware Co-Design for Transformer
VAQF: Fully Automatic Software-Hardware Co-Design Framework for Low-Bit Vision Transformer
Varuna: Scalable, Low-cost Training of Massive Deep Learning Models
ViA: A Novel Vision-Transformer Accelerator Based on FPGA
Work-in-Progress: Utilizing latency and accuracy predictors for efficient hardware-aware NAS
XTC: Extreme Compression for Pre-trained Transformers Made Simple and Efficient
ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers
Fully Unsupervised Machine Translation Using Context-Aware Word Translation and Denoising Autoencoder
DistilHuBERT: Speech representation learning by layer-wise distillation of hidden-unit BERT
Data Movement Reduction for DNN Accelerators: Enabling Dynamic Quantization Through an eFPGA
Hardware-friendly compression and hardware acceleration for transformer: A survey
Hardware/Software Co-Design of Edge DNN Accelerators with TFLite
Workload-Balanced Graph Attention Network Accelerator with Top-K Aggregation Candidates
An Efficient Transformer Inference Engine on DSP
CHARM: Composing Heterogeneous Accelerators for Matrix Multiply on Versal ACAP Architecture
DTATrans: Leveraging Dynamic Token-Based Quantization With Accuracy Compensation Mechanism for Efficient Transformer Architecture
ENEX-FP: A BERT-Based Address Recognition Model
HAMMER: Hardware-friendly Approximate Computing for Self-attention with Mean-redistribution and Linearization
SECDA-TFLite: A toolkit for efficient development of FPGA-based DNN accelerators for edge inference
SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models
Sparse*BERT: Sparse Models Generalize To New tasks and Domains
Teacher Intervention: Improving Convergence of Quantization Aware Training for Ultra-Low Precision Transformers
TiC-SAT: Tightly-Coupled Systolic Accelerator for Transformers
ViTALiTy: Unifying Low-rank and Sparse Approximation for Vision Transformer Acceleration with a Linear Taylor Attention
ViTA: A Vision Transformer Inference Accelerator for Edge Applications
Trends in AI inference energy consumption: Beyond the performance-vs-parameter laws of deep learning
TRON: Transformer Neural Network Acceleration with Non-Coherent Silicon Photonics
TransCODE: Co-design of Transformers and Accelerators for Efficient Training and Inference
TinyVers: A Tiny Versatile System-on-chip with State-Retentive eMRAM for ML Inference at the Extreme Edge
Architecting High Performance Silicon Systems for Accurate and Efficient On-Chip Deep Learning
Hardware-efficient Softmax Approximation for Self-Attention Networks
Fast Prototyping Next-Generation Accelerators for New ML Models using MASE: ML Accelerator System Exploration
Advances in Electromagnetics Empowered by Artificial Intelligence and Deep Learning
A Scalable GPT-2 Inference Hardware Architecture on FPGA
BL-PIM: Varying the Burst Length to Realize the All-Bank Performance and Minimize the Multi-Workload Interference for in-DRAM PIM
Integrated Transformers Inference Framework for Multiple Tenants on GPU
Embedded Deep Learning Accelerators: A Survey on Recent Advances
Collective Communication Enabled Transformer Acceleration on Heterogeneous Clusters
FET-OPU: A Flexible and Efficient FPGA-Based Overlay Processor for Transformer Networks
Racism and Hate Speech Detection on Twitter: A QAHA-Based Hybrid Deep Learning Approach Using LSTM-CNN
Enabling efficient edge intelligence: a hardware-software codesign approach
Automatic Kernel Generation for Large Language Models on Deep Learning Accelerators
A Low-Latency and Lightweight FPGA-Based Engine for Softmax and Layer Normalization Acceleration
PP-Transformer: Enable Efficient Deployment of Transformers Through Pattern Pruning
DEAP: Design Space Exploration for DNN Accelerator Parallelism
Understanding the Potential of FPGA-Based Spatial Acceleration for Large Language Model Inference
An RRAM-Based Computing-in-Memory Architecture and Its Application in Accelerating Transformer Inference
Mobile Transformer Accelerator Exploiting Various Line Sparsity and Tile-Based Dynamic Quantization
A Lightweight Transformer Model using Neural ODE for FPGAs
A Cost-Efficient FPGA Implementation of Tiny Transformer Model using Neural ODE
FlightLLM: Efficient Large Language Model Inference with a Complete Mapping Flow on FPGAs
Accelerating Neural Networks for Large Language Models and Graph Processing with Silicon Photonics
Quantization and Hardware Architecture Co-Design for Matrix-Vector Multiplications of Large Language Models
RDCIM: RISC-V Supported Full-Digital Computing-in-Memory Processor With High Energy Efficiency and Low Area Overhead
A Survey on Hardware Accelerators for Large Language Models
BETA: Binarized Energy-Efficient Transformer Accelerator at the Edge
AttentionLego: An Open-Source Building Block For Spatially-Scalable Large Language Model Accelerator With Processing-In-Memory Technology
SSR: Spatial Sequential Hybrid Architecture for Latency Throughput Tradeoff in Transformer Acceleration
CIM-MLC: A Multi-level Compilation Stack for Computing-In-Memory Accelerators
CIM-MLC: A Multi-level Compilation Stack for Computing-In-Memory Accelerators
The Era of Generative Artificial Intelligence: In-Memory Computing Perspective
Hydragen: High-Throughput LLM Inference with Shared Prefixes
A Survey on Transformer Compression
SLEB: Streamlining LLMs through Redundancy Verification and Elimination of Transformer Blocks
Stochastic Spiking Attention: Accelerating Attention with Stochastic Computing in Spiking Networks
Reusing Softmax Hardware Unit for GELU Computation in Transformers
ConSmax: Hardware-Friendly Alternative Softmax with Learnable Parameters
Speculative Streaming: Fast LLM Inference without Auxiliary Models
H3D-Transformer: A Heterogeneous 3D (H3D) Computing Platform for Transformer Model Acceleration on Edge Devices
NeuPIMs: NPU-PIM Heterogeneous Acceleration for Batched LLM Inferencing
Cerberus: Triple Mode Acceleration of Sparse Matrix and Vector Multiplication
DEFA: Efficient Deformable Attention Acceleration via Pruning-Assisted Grid-Sampling and Multi-Scale Parallel Processing
FastDecode: High-Throughput GPU-Efficient LLM Serving using Heterogeneous Pipelines
Accelerating ViT Inference on FPGA through Static and Dynamic Pruning
Allspark: Workload Orchestration for Visual Transformers on Processing In-Memory Systems
Impact of High-Level-Synthesis on Reliability of Artificial Neural Network Hardware Accelerators
An FPGA-Based Reconfigurable Accelerator for Convolution-Transformer Hybrid EfficientViT
TransFRU: Efficient Deployment of Transformers on FPGA with Full Resource Utilization
PRIMATE: Processing in Memory Acceleration for Dynamic Token-pruning Transformers
SWAT: An Efficient Swin Transformer Accelerator Based on FPGA
VTR: An Optimized Vision Transformer for SAR ATR Acceleration on FPGA
Workload-Aware Hardware Accelerator Mining for Distributed Deep Learning Training
NeuPIMs: NPU-PIM Heterogeneous Acceleration for Batched LLM Inferencing
VITA: ViT Acceleration for Efficient 3D Human Mesh Recovery via Hardware-Algorithm Co-Design
HLSTransform: Energy-Efficient Llama 2 Inference on FPGAs Via High Level Synthesis
SCAR: Scheduling Multi-Model AI Workloads on Heterogeneous Multi-Chiplet Module Accelerators
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent