As deep neural networks (DNNs) continue to scale, inter-layer scheduling, which orchestrates the spatial allocation of compute resources and the temporal execution order across layers, has become a decisive factor in sustaining high utilization and energy efficiency on tiled accelerators. However, existing inter-layer schedulers defer cost feedback until a complete fine-grained intra-layer scheduling has been resolved. The resulting decoupled flow repeatedly explores sub-optimal or even infeasible inter-layer schedules, and the absence of early pruning during the inter-layer phase remains a critical bottleneck for design-space exploration (DSE) in DNN compilers. Our key observation is that the cost of an intra-layer scheduling can be tightly upper-bounded once the inter-layer cut fixes the sub-mesh shape, which lets us cost every inter-layer candidate without solving the intra-layer problem. Hence, we propose a hierarchical partitioning-and-mapping framework, HyperCut, that enables early filtering of inter-layer schedules based on hypergraph partitioning. Based on the directed hypergraph (DHG) abstraction of DNN, we introduce a unified representation, State, that jointly encodes the DHG partition, tile mesh allocation and tensor batch splitting. Thereby, partitioning and mapping are coupled into a union optimization object. For a DNN with N layers, the resulting theoretical design space is bounded by O(N), compared with O(9.899^N) for the state-of-the-art open-source scheduler SET. Across 10 evaluated cases, HyperCut achieves 2.0x performance improvement and 80.47% exploration time reduction over the SET baseline, measured by geometric mean.
Ziang Wei, Zirui Xu, Sufeng Guo et al.· 0 citations
This study introduces a unified control mechanism for integrated charging and service allocation of Mobility-on-Demand Electric Vehicles (MoD-EVs) operating under a flexible A-to-B rental model. The charging operation of MoD-EVs is influenced by stochastic customer rental requests and time-varying electricity prices. The framework captures the nonlinear battery dynamics and optimizes the charging operations against dynamic electricity prices and to meet stochastic customer demand while prioritizing battery health and cost efficiency. To address the computational challenges posed by the nonlinear battery dynamics, the framework employs a piece-wise linear model integrated into a multi-objective, chance-constrained mixed-integer linear programming (MILP) Model Predictive Control (MPC) formulation. A mixed logical switching mechanism is utilized to determine optimal charging sequences. Furthermore, a distributed approach is implemented to ensure computational scalability compared to centralized alternatives. Evaluation of this approach using a state-of-the-art commercial solver with stochastic EV rental requests under different confidence levels and time-varying electricity prices demonstrates significant benefits of the integrated mechanism design, including a reduction in charging costs and battery capacity degradation compared to the prevailing business-as-usual (BAU) approach and state of the art Laxity-based charging (LC) approach.
This work proposes neurosymbolic HRL with {\em Incremental Knowledge (InK), where symbolic high-level components perform symbolic planning on an updatable representation of current InK, while low-level goal-conditioned neural modules learn motion primitives through experience using reward shaping.
Subrat Prasad Panda, B. Genest, A. Easwaran· 0 citations