Constraint-Driven Auto-Tuning of GEMM-like Operators for MT-3000 Many-core Processor

Optimizing deep learning (DL) operators, particularly GEMM-like operations, for emerging heterogeneous many-core processors like MT-3000 is challenging due to the large search space and hardware-specific constraints. Existing approaches - such as hand-crafted libraries or general-purpose auto-tuners - are either expensive to develop or deliver sub-optimal performance due to expensive search overheads. We present DynaChain, an operator-level optimization framework for MT-3000. DynaChain decouples the computation and data movement of operators, allowing each to be optimized independently and maximizing global data reuse across the operator schedule. To reduce the search space, DynaChain introduces constraint dependency chains that dynamically eliminate invalid scheduling options during exploration. It then applies an integer linear programming (ILP) based decomposition to handle irregular matrix dimensions, avoiding padding and improving hardware utilization. For low-level code generation, DynaChain offers a hardware-aware micro-kernel design optimized for the MT-3000’s VLIW+SIMD architecture, supporting irregular operations through improved register allocation and instruction pipelining. Experimental results on a range of representative DL operators demonstrate that DynaChain simplifies kernel development for heterogeneous many-core architectures while delivering performance on par with expert-optimized libraries.