FlexiGen : An Automated AI Accelerator Generation Framework With Decoupled-Access-Execute and Dynamic Dataflows

Modern tensor applications, especially artificial intelligence (AI) applications, are evolving rapidly, posing a significant demand for agile hardware design. While numerous hardware generators have been developed, they suffer from three significant limitations: 1) they are either limited to a single dataflow/data type generation, failing to cater to the computational requirements of diverse workloads; 2) or focus only on the array level optimization, omitting system-level effects, such as the influence of on-chip memory bandwidth and contention; and 3) customized workload mapping/configuration is needed, resulting in increased programming complexity. To address these challenges, we proposeFlexiGen, a flexible and extensible hardware generation framework, which targets diverse deep neural networks (DNN) tensor applications and can generate a complete synthesizable acceleration system at the RTL level with arbitrary dataflow and its combinations. Our key contributions are threefold: 1) we incorporate decoupled-access-execute architecture insideFlexiGen, enabling full system generation while maintaining flexibility and efficiency; 2) we propose a versatile spatial core generator that supports dynamic spatial dataflows and multiple data precisions in the same array and a compatible data streaming engine generator that can support arbitrary temporal dataflows and$N$-dimensional data access patterns; and 3) we leverage a uniform programming interface and provide a customized kernel library, enabling agile configuration programming. We conduct an intensive evaluation to demonstrate the versatility ofFlexiGenin dataflow accelerator generation and show the trade-offs of performance, area, and power across a wide range of dataflows and workloads at both the array level and system level. Our case study experiment showsFlexiGen’s usefulness as a hardware generator to rapidly generate desired dataflow acceleration systems. Compared with the state-of-the-art (SotA) hardware generation framework LEGO,FlexiGenachieves 36.79% and 57.16% less area and power when generating the same dual spatial dataflow design.FlexiGenis open-source and available athttps://github.com/KULeuven-MICAS/snax_cluster