EdgeBERT: Sentence-Level Energy Optimizations for Latency-Aware Multi-Task NLP Inference
arXiv:2011.14203
Abstract
Transformer-based language models such as BERT provide significant accuracy improvement for a multitude of natural language processing (NLP) tasks. However, their hefty computational and memory demands make them challenging to deploy to resource-constrained edge platforms with strict latency requirements. We present EdgeBERT, an in-depth algorithm-hardware co-design for latency-aware energy optimization for multi-task NLP. EdgeBERT employs entropy-based early exit predication in order to perform dynamic voltage-frequency scaling (DVFS), at a sentence granularity, for minimal energy consumption while adhering to a prescribed target latency. Computation and memory footprint overheads are further alleviated by employing a calibrated combination of adaptive attention span, selective network pruning, and floating-point quantization. Furthermore, in order to maximize the synergistic benefits of these algorithms in always-on and intermediate edge computing settings, we specialize a 12nm scalable hardware accelerator system, integrating a fast-switching low-dropout voltage regulator (LDO), an all-digital phase-locked loop (ADPLL), as well as, high-density embedded non-volatile memories (eNVMs) wherein the sparse floating-point bit encodings of the shared multi-task parameters are carefully stored. Altogether, latency-aware multi-task NLP inference acceleration on the EdgeBERT hardware system generates up to 7x, 2.5x, and 53x lower energy compared to the conventional inference without early stopping, the latency-unbounded early exit approach, and CUDA adaptations on an Nvidia Jetson Tegra X2 mobile GPU, respectively.
arXiv comment12 pages plus references. Paper to appear at the 54th IEEE/ACM International Symposium on Microarchitecture (MICRO 2021)
References in corpus (8)
- HuggingFace's Transformers: State-of-the-art Natural Language Processing
- Q8BERT: Quantized 8Bit BERT
- SpAtten: Efficient Sparse Attention Architecture with Cascade Token and Head Pruning
- TinyLSTMs: Efficient Neural Speech Enhancement for Hearing Aids
- Rethinking floating point for deep learning
- FixyNN: Efficient Hardware for Mobile Computer Vision via Transfer Learning
- AdaptivFloat: A Floating-point based Data Type for Resilient Deep Learning Inference
- Hardware Accelerator for Multi-Head Attention and Position-Wise Feed-Forward in the Transformer