Summary Chiplet Cloud Building AI Supercomputers for Serving Large Generative Language Models arxiv.org
10,852 words - PDF document - View PDF document
One Line
Chiplet Cloud is a cost-effective and energy-efficient AI-supercomputer architecture that utilizes replicated chiplet accelerator modules to focus on the transformer decode block for large generative language models.
Slides
Slide Presentation (10 slides)
Key Points
- Chiplet Cloud is a chiplet-based ASIC AI-supercomputer architecture designed for large generative language models.
- The architecture aims to reduce capital expenditure and energy consumption compared to traditional systems like GPT-3.
- The use of on-chip SRAM is favored over DDR4 and HBM2e for storing model parameters due to better bandwidth and read energy.
- The Chiplet Cloud system design breaks down a monolithic silicon chip into multiple small chiplets, improving fabrication yield and reducing manufacturing costs.
- The Chiplet Cloud design methodology consists of hardware exploration and software evaluation phases to optimize the total cost of ownership (TCO).
- The system utilizes pipeline parallelism and supports batch sizes up to 64 for multi-head models and up to 1024 for multi-query models.
- The architecture optimizes the attention block and eliminates bandwidth limitations by fitting all model parameters inside on-chip memory.
- Relevant papers on efficient language model training, scaling transformer inference, and specific models like ChatGPT and Megatron-LM are mentioned.
Summaries
32 word summary
Chiplet Cloud is an efficient chiplet-based AI-supercomputer architecture that reduces costs and energy consumption by using replicated chiplet accelerator modules, specifically for large generative language models. It emphasizes the transformer decode block.
39 word summary
Chiplet Cloud is a chiplet-based ASIC AI-supercomputer architecture designed to efficiently serve large generative language models. It aims to reduce capital expenditure and energy consumption by utilizing replicated chiplet accelerator modules. The architecture focuses on the transformer decode block,
513 word summary
The paper proposes Chiplet Cloud, a chiplet-based ASIC AI-supercomputer architecture that aims to efficiently serve large generative language models. The architecture utilizes replicated chiplet accelerator modules to perform token generation with a low total cost of ownership (TCO
GPT-3 requires a significant number of servers and GPUs to keep up with the demand, resulting in high capital expenditure and energy consumption. To address this, the Chiplet Cloud proposes a chiplet-based ASIC AI-supercomputer architecture that reduces the
The architecture of a generative language model is built around the transformer decode block, with each block defining a layer of the model. Fully connected (FC) layers dominate the runtime of the decode block in large language models. Generative language models use aut
The low operational intensity of GeMM in FC layers creates a bottleneck in memory bandwidth when reading KV cache. SRAM has better bandwidth and read energy compared to DDR4 and HBM2e, making it a more favorable option for storing model parameters.
The current systems for large generative language models (LLMs) have low utilization and high chip fabrication costs. The capital expenditures account for a significant portion of the total cost of ownership (TCO). To address this, the use of on-chip
Chiplet Cloud is a chiplet-based cloud-scale system design for large generative language models (LLMs) inference. It breaks down a traditional monolithic silicon chip into multiple small chiplets, improving fabrication yield, reducing manufacturing costs, and enabling die
The authors propose a design methodology called Chiplet Cloud for optimizing the total cost of ownership (TCO) of large-scale systems that serve generative language models. The methodology consists of two phases: hardware exploration and software evaluation. In the hardware exploration phase
The software evaluation flow involves using realizable server design points and a generative LLM specification to perform software optimized inference simulations and TCO estimations. This helps find Pareto optimal Chiplet Cloud design points. The system mapping determines the portion of the
The document discusses the design methodology for building AI supercomputers that serve large generative language models. The methodology involves considering various factors such as hardware design, software mapping, cost, and performance. Design points are determined based on these factors, taking into
Chiplet Cloud is a system designed for serving large generative language models. The system utilizes pipeline parallelism to improve system utilization. It supports batch sizes up to 64 for multi-head models and batch sizes up to 1024 for the multi-query
The performance of 10 servers on different models and context lengths is plotted and normalized to the optimal performance. References to relevant papers are provided, including ones on DeepSpeed-Inference, Pathways, Language Models as Few-Shot Learners, PaLM
Chiplet Cloud is an ASIC AI supercomputer architecture that focuses on serving large generative language models. It optimizes the attention block and leverages chiplet technology to improve scalability. The architecture eliminates bandwidth limitations by fitting all model parameters inside on-chip
Efficient large-scale language model training on GPU clusters using Megatron-LM is discussed in [23]. The introduction of ChatGPT by OpenAI is mentioned in [24]. Efficiently scaling transformer inference is explored in [25]. A 1
The summary is not provided.