SCALE-Sim EVA: Design Principles for an Extensible, Visualizable, and Adaptable Accelerator Simulation Framework

Abstract

Modern AI accelerators increasingly combine heterogeneous compute units, hierarchical memories, local buffers, and specialized data movement paths. This diversity makes fixed accelerator simulators difficult to extend beyond their original execution model. We present SCALE-Sim EVA, an extensible, visualizable, and adaptable simulation framework for IR-aware accelerator modeling. EVA represents workloads as tensor-based commands, tracks runtime tensor placement and readiness, and executes commands on composable hardware components with user-defined functional units and memory behavior. Instead of replaying address-level cycle traces, EVA computes cycle timing from tensor readiness, hardware-unit availability, and modeled operation or transfer latency. Its command decomposition mechanism bridges compiler-level IR granularity and hardware-level execution granularity, allowing global tensor operations to be lowered and scheduled locally across a hardware hierarchy. EVA also emits open command and storage traces for visual analysis of execution timelines, memory occupancy, and resource contention.