AccelForge: Comprehensive Modeling and Co-Design Framework for AI Accelerators

2026-09-10

AI Summary

为解决AI加速器设计评估难题，本文提出AccelForge框架，通过提供可组合模型、快速映射器及易用的Python实现来优化加速器设计与工作负载映射。

Abstract

Tensor algebra workloads, of which deep neural networks are prominent examples, are energy-intensive workloads in modern datacenter and edge deployments, making accelerators necessary to achieve energy efficiency and high throughput. To quickly evaluate and iterate on accelerator designs, we need an accelerator modeling framework that captures salient attributes of devices, circuits, architectures, workloads, as well as optimizing the mapping of the workload onto the hardware. In this paper, we introduce AccelForge, which improves upon existing accelerator modeling frameworks in capabilities, speed, and ease-of-use. AccelForge unifies and multiple works into one framework, and it includes (1) composable user-defined and user-modifiable models of devices, circuits, and architectures, (2) fast mappers that enable accurate evaluation in orders of magnitude less (computer and human) time, and (3) easy-to-use and easy-to-extend, yet still high performance, Python implementations of both the model and mapper to enable rapid research and extension to novel optimizations.

Problem

• AI Accelerators
• Tensor Algebra Workloads
• Energy Efficiency
• High Throughput
• Accelerator Modeling Framework

Innovation

• Composable Models
• Fast Mappers
• Ease-of-Use and Extension