Systematic Design Methodologies for Multi-Engine Deep Learning Accelerators

Doctoral thesis, 2026

Domain-Specific Accelerators (DSAs) have become a key driver of performance and efficiency improvements in the post-Moore's Law era. These improvements stem from specializing the hardware for domain workloads and exploiting the workloads' inherent parallelism. In the domain of Deep Learning (DL), workloads (models) comprise multiple operations, known as layers, that exhibit parallelism opportunities and diverse computational characteristics. Consequently, DSAs with multiple computational units (engines) provide a natural architectural paradigm to fully exploit the specialization and parallelism potential inherent in such multi-layered models.


Multi-engine DL accelerators generally fall into two categories: model-specific and flexible. Model-specific accelerators are co-designed to efficiently execute one or a few closely related models. Flexible accelerators, by contrast, are designed to support a broad range of DL workloads. Designing and implementing accelerators in either category that fully exploit specialization and parallelism, and thus optimize performance and efficiency, requires systematic exploration based on quantitative evaluation of design alternatives. Existing multi-engine DL accelerator design approaches range from intuition-driven to exploration-based methodologies. However, even the latter typically leave key architectural parameters unexplored by fixing them a priori based on expert knowledge and intuition. In many cases, the fixed parameters are more consequential for accelerator specialization and parallelism than the explored parameters. Consequently, the full potential of the multi-engine paradigm is often left unexploited.


To fully exploit the potential of multi-engine DL accelerators, this thesis presents design methodologies that increase the extent to which key design choices are based on exploration and quantitative evaluation of design alternatives. The first contribution of this thesis is the Fixed Budget Hybrid CNN Accelerator (FiBHA). FiBHA proposes a hybrid, model-specific, multi-engine architecture and an accompanying design methodology. FiBHA targets a specific class of DL models and relies primarily on empirical analysis to exploit opportunities for specialization and parallelism. To expand the scope and co-design accelerators for a broader class of models, the work moved to a more analytical approach. The second contribution of this thesis comprises MCCM, a fast analytical cost model for evaluating model-specific multi-engine accelerators, and MCExplorer, a design space exploration framework built upon it. Together, they enable orders-of-magnitude faster evaluation and systematic exploration of model-specific multi-engine accelerator designs. Unlike existing approaches that rely on predefined design choices, MCExplorer quantitatively evaluates alternative architectural configurations across a broader design space. To further expand the scope, the work extends to flexible, in addition to model-specific, multi-engine accelerators. The third contribution of the thesis is a design methodology for flexible multi-engine DL accelerators, termed MEDEM. To support a wide range of diverse DL workloads, a flexible multi-engine accelerator must have an engine combination with complementary capabilities to ensure that different layers across these diverse workloads are processed efficiently. Existing work builds flexible multi-engine accelerators by combining expert-selected, independently optimized engines. However, independently optimized engines may perform best on largely overlapping subsets of workloads, and thus their combination does not necessarily improve overall workload coverage. MEDEM presents an alternative design methodology where the engines are co-designed, then curated to find a combination that maximizes the coverage of diverse workloads.


Using a systematic approach based on modeling and quantitative evaluation of a wider space of design alternatives, the proposed methodologies identify accelerator architectures that better exploit the specialization and parallelism inherent in DL workloads. This applies to both model-specific accelerators, as FiBHA, MCCM, and MCExplorer demonstrate, and to flexible ones, as MEDEM shows. As a result, these methodologies identify architectures that consistently outperform the state-of-the-art, achieving considerable improvements in latency, throughput, energy, and energy-delay product (EDP).