Research and Materials on Hardware implementation of BERT (Bidirectional Encoder Representations from Transformers) Model
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, including pushing the GLUE score to 80.5% (7.7% point absolute improvement), MultiNLI accuracy to 86.7 (5.6% absolute improvement), SQuAD v1.1 question answering Test F1 to 93.2 (1.5% absolute improvement) and SQuAD v2.0 Test F1 to 83.1 (5.1% absolute improvement).
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).
Distilling the Knowledge in a Neural Network
Distilling Knowledge Learned in BERT for Text Generation
DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
TinyBERT: Distilling BERT for Natural Language Understanding
Distilling the Knowledge in a Neural Network
FastBERT: a Self-distilling BERT with Adaptive Inference Time
Distilling Task-Specific Knowledge from BERT into Simple Neural Networks
Patient Knowledge Distillation for BERT Model Compression
MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited Devices
Improving Multi-Task Deep Neural Networks via Knowledge Distillation for Natural Language Understanding
Algorithm-Hardware Co-Design of Single Shot Detector for Fast Object Detection on FPGAs
An Evaluation of Transfer Learning for Classifying Sales Engagement Emails at Large Scale
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^3: Accelerating Attention Mechanisms in Neural Networks with Approximation
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
Deep Learning Acceleration with Neuron-to-Memory Transformation
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
Look-Up Table based Energy Efficient Processing in Cache Support for Neural Network Acceleration
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
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
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
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
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
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
Kunlun: A 14nm High-Performance AI Processor for Diversified Workloads
Layerweaver: Maximizing Resource Utilization of Neural Processing Units via Layer-Wise Scheduling
M2M: Learning to Enhance Low-Light Image from Model to Mobile FPGA
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
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
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 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 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
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
Elastic Processing and Hardware Architectures for Machine Learning
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
Fast Heterogeneous Task Mapping for Reducing Edge DNN Latency
FILM-QNN: Efficient FPGA Acceleration of Deep Neural Networks with Intra-Layer, Mixed-Precision Quantization
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
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
LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale
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
Near-Optimal Sparse Allreduce for Distributed Deep Learning
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
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
ViA: A Novel Vision-Transformer Accelerator Based on FPGA
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
HAMMER: Hardware-friendly Approximate Computing for Self-attention with Mean-redistribution and Linearization
ViTA: A Vision Transformer Inference Accelerator for Edge Applications
TRON: Transformer Neural Network Acceleration with Non-Coherent Silicon Photonics
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
Trends in AI inference energy consumption: Beyond the performance-vs-parameter laws of deep learning
TransCODE: Co-design of Transformers and Accelerators for Efficient Training and Inference
Varuna: Scalable, Low-cost Training of Massive Deep Learning Models
