Search Results for author:
Found 10 papers, 3 papers with code
AI and Memory Wall
The availability of unprecedented unsupervised training data, along with neural scaling laws, has resulted in an unprecedented surge in model size and compute requirements for serving/training LLMs.
KVQuant: Towards 10 Million Context Length LLM Inference with KV Cache Quantization
LLMs are seeing growing use for applications such as document analysis and summarization which require large context windows, and with these large context windows KV cache activations surface as the dominant contributor to memory consumption during inference.
Learned Best-Effort LLM Serving
Many applications must provide low-latency LLM service to users or risk unacceptable user experience.
S-LoRA: Serving Thousands of Concurrent LoRA Adapters
To capitalize on these opportunities, we present S-LoRA, a system designed for the scalable serving of many LoRA adapters.
Property-Aware Multi-Speaker Data Simulation: A Probabilistic Modelling Technique for Synthetic Data Generation
This capability offers a tailored training environment for developing neural models suited for speaker diarization and voice activity detection.
SPEED: Speculative Pipelined Execution for Efficient Decoding
For Transformer decoders that employ parameter sharing, the memory operations for the tokens executing in parallel can be amortized, which allows us to accelerate generative LLM inference.
SqueezeLLM: Dense-and-Sparse Quantization
When applied to the LLaMA models, our 3-bit quantization significantly reduces the perplexity gap from the FP16 baseline by up to 2. 1x as compared to the state-of-the-art methods with the same memory requirement.
Full Stack Optimization of Transformer Inference: a Survey
In this work, we survey different approaches for efficient Transformer inference, including: (i) analysis and profiling of the bottlenecks in existing Transformer architectures and their similarities and differences with previous convolutional models; (ii) implications of Transformer architecture on hardware, including the impact of non-linear operations such as Layer Normalization, Softmax, and GELU, as well as linear operations, on hardware design; (iii) approaches for optimizing a fixed Transformer architecture; (iv) challenges in finding the right mapping and scheduling of operations for Transformer models; and (v) approaches for optimizing Transformer models by adapting the architecture using neural architecture search.
Quantifying and Maximizing the Benefits of Back-End Noise Adaption on Attention-Based Speech Recognition Models
This work analyzes how attention-based Bidirectional Long Short-Term Memory (BLSTM) models adapt to noise-augmented speech.
EdgeBERT: Sentence-Level Energy Optimizations for Latency-Aware Multi-Task NLP Inference
Transformer-based language models such as BERT provide significant accuracy improvement for a multitude of natural language processing (NLP) tasks.
