Efficient End-to-End Acceleration of Autoregressive Transformer Models using Hybrid Process-in-Memory Architecture

Core Concepts

A hybrid process-in-memory (PIM) accelerator, PIM-GPT, achieves state-of-the-art performance and energy efficiency for autoregressive Transformer models like GPT by leveraging PIM for memory-intensive operations and an ASIC for other computations.

Abstract

The paper proposes PIM-GPT, a hybrid hardware-software solution to efficiently accelerate autoregressive Transformer models like GPT. At the hardware level, PIM-GPT consists of DRAM-based PIM chips to accelerate vector-matrix multiplication (VMM) operations near data, and an application-specific integrated circuit (ASIC) to handle other computations like non-linear functions and data communication. The key aspects of the PIM-GPT design are: Mapping scheme to maximize data locality and computation parallelism by partitioning and distributing matrices across DRAM channels and banks. Pipelining data transmission and computation between the PIM chips and ASIC to minimize latency. Reserving space in DRAM banks to store intermediate data like Key and Value matrices required for attention computation. Evaluation shows PIM-GPT achieves 41-137x speedup and 123-383x energy efficiency over GPU, and 631-1074x speedup and 320-602x energy efficiency over CPU for 8 GPT models with up to 1.4 billion parameters. The design only requires light modifications to the DRAM architecture, making it a practical and efficient solution for accelerating memory-bounded autoregressive Transformer models.

Stats

GPT3-XL has 1.15 billion parameters, over 100x more than common CNNs like ResNet-18. The arithmetic intensity (ops/parameter) of GPT is 2.1, much lower than 48.4 in ResNet-18. PIM-GPT achieves 41-137x speedup and 123-383x energy efficiency over GPU. PIM-GPT achieves 631-1074x speedup and 320-602x energy efficiency over CPU.

Quotes

"Decoder-only Transformer models such as GPT have demonstrated exceptional performance in text generation, by autoregressively predicting the next token." "Compared to CNNs, GPT has two main features : (1) extremely large model size and (2) low compute-to-memory-ratio." "DRAM-based process-in-memory (PIM) is a promising architecture to accelerate memory-bounded tasks."

Key Insights Distilled From

PIM-GPT: A Hybrid Process-in-Memory Accelerator for Autoregressive Transformers

by Yuting Wu,Zi... at arxiv.org 04-16-2024

https://arxiv.org/pdf/2310.09385.pdf

Deeper Inquiries

How can the PIM-GPT design be extended to support other types of Transformer models beyond GPT, such as encoder-decoder models

To extend the PIM-GPT design to support other types of Transformer models beyond GPT, such as encoder-decoder models like BERT, several modifications and adaptations can be made: Mapping Scheme Adjustments: The mapping scheme in PIM-GPT can be optimized to accommodate the specific architecture and data flow patterns of encoder-decoder models. This may involve reconfiguring how weights and input data are distributed across PIM channels and banks to suit the computation requirements of encoder-decoder models. ASIC Functionality Expansion: The ASIC component of PIM-GPT can be enhanced to include additional functions and operations specific to encoder-decoder models. This may involve incorporating specialized modules for tasks like attention mechanisms, cross-attention, and other operations unique to encoder-decoder architectures. Dataflow Optimization: The dataflow within the PIM-GPT system can be restructured to support the bidirectional nature of encoder-decoder models. This includes ensuring efficient communication between the encoder and decoder components, as well as handling the flow of information in both directions during inference. Scalability Considerations: The design should be scalable to handle the larger memory and computation requirements of encoder-decoder models, which often have more parameters and complex architectures compared to decoder-only models like GPT. This may involve increasing the number of PIM channels, banks, and ASIC capabilities to accommodate the additional workload. By adapting the mapping scheme, expanding ASIC functionality, optimizing dataflow, and ensuring scalability, the PIM-GPT design can be extended to effectively support a wide range of Transformer models, including encoder-decoder architectures like BERT.

What are the potential challenges and trade-offs in further scaling the PIM-GPT system to support even larger Transformer models with billions more parameters

Scaling the PIM-GPT system to support even larger Transformer models with billions more parameters presents several challenges and trade-offs: Memory and Computation Requirements: Larger Transformer models require significantly more memory and computational resources. Scaling the PIM-GPT system to handle these requirements may necessitate increasing the number of PIM channels, banks, and ASIC capabilities, which can lead to higher power consumption and complexity. Data Locality and Parallelism: Ensuring efficient data locality and parallelism becomes more challenging as the model size increases. Balancing the distribution of weights and data across PIM channels and banks while maintaining high computation parallelism becomes more complex with larger models. Latency and Throughput: Larger models may introduce latency issues due to the increased data movement and processing requirements. Trade-offs between latency and throughput need to be carefully managed to ensure optimal performance while handling the massive scale of the model. ASIC Flexibility: The ASIC component of the system may need to be more flexible and adaptable to support the diverse operations and computations required by larger Transformer models. Designing a versatile ASIC that can efficiently handle the varied tasks of different model architectures is crucial. By addressing these challenges and carefully managing the trade-offs between scalability, performance, and complexity, the PIM-GPT system can be scaled to support even larger Transformer models with billions more parameters.

Given the rapid progress in Transformer models, how can the PIM-GPT architecture be made more flexible and adaptable to efficiently accelerate future generations of autoregressive language models

To make the PIM-GPT architecture more flexible and adaptable to efficiently accelerate future generations of autoregressive language models, several strategies can be implemented: Modular Design: Implement a modular design approach that allows for easy integration of new components and functionalities. This modularity enables the system to adapt to evolving requirements and accommodate different types of Transformer models without requiring extensive redesign. Dynamic Mapping Schemes: Develop dynamic mapping schemes that can adjust to the specific characteristics and computational needs of different language models. This flexibility in mapping allows the system to optimize data locality and computation parallelism based on the model's architecture. Hardware Reconfigurability: Design the PIM-GPT system with reconfigurable hardware components that can be dynamically adjusted to support varying model sizes, complexities, and operations. This reconfigurability enhances the system's adaptability to different autoregressive language models. Software Abstraction Layers: Implement software abstraction layers that decouple the hardware functionalities from the model-specific operations. This abstraction allows for easier integration of new models, as the software layer can adapt the system's hardware resources to meet the requirements of different models. By incorporating these strategies, the PIM-GPT architecture can be made more flexible, adaptable, and future-proof to efficiently accelerate a wide range of autoregressive language models in the rapidly evolving landscape of natural language processing.