Allo: A Composable Programming Model for Efficient Spatial Accelerator Design

Core Concepts

Allo provides a composable programming model that decouples hardware customizations from algorithm specifications, enabling progressive and verifiable transformations to construct high-performance spatial accelerator designs.

Abstract

The paper introduces Allo, a new programming model for composable design of high-performance spatial accelerator architectures. Allo decouples hardware customizations, including compute, memory, communication, and data type, from algorithm specification, and encapsulates them as a set of customization primitives. Allo preserves the hierarchical structure of an input program by combining customizations from different functions in a bottom-up, type-safe manner, facilitating holistic optimizations that span across function boundaries.
The key features of Allo include:

Progressive hardware customizations: Allo provides decoupled hardware customization primitives, allowing users to progressively transform a vanilla program into a high-performance design, with each step being verifiable.
Composable schedules: Allo enables users to construct modular hardware accelerators from the ground up by combining customized kernels and external IPs. A type system for the memory layout is also proposed to ensure type safety during schedule composition.
Holistic dataflow optimizations: Allo introduces a hierarchical dataflow graph to support the composition of multiple kernels within a complex design while maintaining the function boundaries. It ensures the correctness of the interfaces when integrating distinct kernels and effectively sizes the streaming buffers (FIFOs) between stages.

The authors conduct comprehensive experiments on both realistic benchmarks and large neural networks. For PolyBench, Allo outperforms several state-of-the-art HLS tools and ADLs. Furthermore, Allo is applied to the evaluation of large language models (LLMs) on an FPGA, demonstrating a 1.7x speedup and 5.4x higher energy efficiency on the GPT2 model compared to the A100 GPU.

Stats

The latency of the vanilla GEMM implementation is 25,074 ms.
The latency of the inner-product GEMM implementation is 17,950 ms.
The latency of the row-wise product GEMM implementation is 112 ms.
The latency of the simple cascade of two GEMM kernels is 280 ms.
The latency of the interface-unified cascade of two GEMM kernels is 224 ms.

Quotes

"Existing HLS tools often require intrusive source-level changes to attain satisfactory quality of results."
"Existing ADLs prove less effective for realistic hierarchical designs with multiple kernels, even if the design hierarchy is flattened."
"Allo decouples hardware customizations, including compute, memory, communication, and data type from algorithm specification, and encapsulates them as a set of customization primitives."
"Allo preserves the hierarchical structure of an input program by combining customizations from different functions in a bottom-up, type-safe manner."

Key Insights Distilled From

Allo

by Hongzheng Ch... at arxiv.org 04-09-2024

https://arxiv.org/pdf/2404.04815.pdf

Deeper Inquiries

How can Allo's composable schedules and holistic dataflow optimizations be extended to support dynamic-sized inputs and outputs

To extend Allo's composable schedules and holistic dataflow optimizations to support dynamic-sized inputs and outputs, several adjustments and enhancements can be implemented.

Dynamic Memory Allocation: Allo can incorporate mechanisms for dynamic memory allocation to handle varying input and output sizes. By allowing for dynamic resizing of memory buffers based on the input dimensions, Allo can adapt to different data sizes at runtime.

Parameterized Templates: Introducing parameterized templates for kernel designs can enable the creation of flexible designs that can accommodate dynamic input and output sizes. By defining templates with parameters for input and output dimensions, Allo can generate customized designs on-the-fly based on the specific input requirements.

Runtime Configuration: Implementing runtime configuration options within Allo can allow users to specify input and output sizes dynamically during program execution. This runtime flexibility can enable Allo to adjust the hardware design on the fly based on the actual input data dimensions.

Dataflow Analysis: Enhancing Allo's dataflow analysis capabilities to handle variable-sized data streams can ensure efficient data movement and processing. By dynamically optimizing dataflow paths based on the input and output sizes, Allo can maximize performance for varying data dimensions.

By incorporating these features and enhancements, Allo can effectively support dynamic-sized inputs and outputs while maintaining its composable schedules and holistic dataflow optimizations.

What are the potential challenges in applying Allo's programming model to other hardware accelerator architectures beyond spatial designs, such as dataflow or von Neumann-style architectures

Applying Allo's programming model to hardware accelerator architectures beyond spatial designs, such as dataflow or von Neumann-style architectures, may present several challenges:

Dataflow Management: Dataflow architectures rely on efficient data movement and synchronization mechanisms, which may require specialized optimizations not directly supported by Allo's current primitives. Adapting Allo to handle complex dataflow patterns and dependencies inherent in dataflow architectures could be a significant challenge.

Instruction-Level Parallelism: Von Neumann architectures often involve intricate instruction-level parallelism and memory access patterns that may not align with Allo's current focus on spatial designs. Extending Allo to effectively optimize for instruction-level parallelism and memory hierarchies typical in von Neumann architectures would require significant enhancements.

Control Flow Handling: Dataflow and von Neumann architectures involve different control flow mechanisms compared to spatial designs. Allo would need to accommodate these variations in control flow and branching to effectively optimize hardware designs for such architectures.

Resource Management: Managing resources such as registers, memory, and functional units in dataflow and von Neumann architectures differs from spatial designs. Allo would need to incorporate new primitives and optimizations to handle resource allocation and utilization in these alternative architectures.

Addressing these challenges would require a comprehensive reevaluation and extension of Allo's capabilities to cater to the unique requirements of dataflow and von Neumann-style architectures.

How can Allo's type system and type-safe composition be leveraged to enable automated design space exploration and hardware-software co-optimization

Allo's type system and type-safe composition can be leveraged to enable automated design space exploration and hardware-software co-optimization in the following ways:

Automated Parameter Tuning: By utilizing the type system to define parameterized templates for hardware designs, Allo can automate the tuning of design parameters based on performance metrics. This automated parameter tuning can optimize hardware designs for specific applications without manual intervention.

Hardware-Software Interface Verification: Allo's type system can ensure compatibility between hardware accelerators and software components by enforcing type safety. This verification mechanism can prevent interface mismatches and facilitate seamless hardware-software co-optimization.

Design Space Exploration: Leveraging the type system to define constraints and properties of hardware components, Allo can automate the exploration of design space by systematically varying parameters and configurations. This automated exploration can identify optimal design choices for performance and efficiency.

Feedback-Driven Optimization: Allo's type-safe composition can enable feedback-driven optimization, where performance data from hardware implementations is used to refine and optimize the design automatically. This iterative process can lead to continuous improvement in hardware designs based on real-world performance metrics.

By integrating these strategies, Allo can streamline the design process, enhance hardware-software co-optimization, and facilitate automated exploration of the design space for efficient and effective hardware accelerator development.