TensorLift: Automatic Extraction of Tensor-Level ISA Semantics from Accelerator RTL via MLIR Semantic Lifting

Abstract

Most proposed tensor accelerators lack well-documented ISAs and compiler backends, and are exercised only through hand-written kernels covering a handful of operators. Recent work (TAIDL, ACT) shows that a tensor-level ISA specification is enough to generate complete software stacks automatically. Writing that specification, however, remains a manual, expert-driven process. We present TensorLift, the first end-to-end MLIR-based pipeline that lifts RTL-extracted accelerator semantics to TAIDL-like tensor ISA specifications. Building on prior architecture-level model extraction that yields bit-level IR, an 8-pass MLIR pipeline progressively recovers tensor structure (MAC idioms, saturation semantics, multi-dimensional buffer organizations, and layout transformations) and emits specifications the ACT ecosystem consumes directly. On Gemmini and VTA, TensorLift captures every hardware instruction the two designs decode across 156 MLIR files, reducing the extracted bit-level MLIR by 24.8% and 41.2% overall and by up to 92.9% on a processing element, with controller modules retaining irreducible control logic. It recovers hardware features the hand-written reference omits (multi-bank DMA configuration, pooling, and im2col) and lifts VTA unmodified. Core compute and data-movement semantics are proven equivalent to the RTL-extracted model by Z3 SMT, the remainder validated against golden simulator data. Fed into ACT, the extracted specification yields a compiler backend at parity with hand-written Gemmini kernels (1.014x geometric mean), giving an automated path from RTL to a working software stack.