AI & ML
vTen: Tensor-Centric Verification Framework for Domain-Specific Accelerators
arXiv cs.DC0 views
arXiv:2610.08372v1 Announce Type: new Abstract: The semantic gap between tensor-centric software models and signal-level hardware testbenches creates significant productivity bottlenecks in verifying Domain-Specific Accelerators (DSAs). Existing frameworks like Cocotb suffer from prohibitive synchronization overheads due to fine-grained interactions. To address this, we propose vTen, a data-centric framework that strictly decouples verification intent from execution mechanics. By leveraging a declarative DSL and kernel-granular batching, vTen minimizes host-simulator interaction frequency. Evaluation on a production-scale 3D U-Net accelerator demonstrates that vTen achieves a 2x performance improvement in simulation latency and a 60.3% reduction in code complexity compared to Cocotb.
Source:https://arxiv.org/abs/2610.08372PreviousGPU Acceleration of Awkward Arrays: Using Python cuda.computeNextNeMo-DCR: Bit-Exact Delta-Compressed Refit for Scalable Agentic RL at Trillion-Parameter Scale
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