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Continual hashing for efficient fine-grain state inconsistency detection

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3 Author(s)
Lee, J.W. ; Comput. Sci. & Artificial Intell. Lab., Massachusetts Inst. of Technol., Cambridge, MA ; King, Myron ; Asanovic, K.

Transaction-level modeling (TLM) allows a designer to save functional verification effort during the modular refinement of an SoC by reusing the prior implementation of a module as a golden model for state inconsistency detection. One problem in simulation-based verification is the performance and bandwidth overhead of state dump and comparison between two models. In this paper, we propose an efficient fine-grain state inconsistency detection technique that checks the consistency of two states of arbitrary size at sub- transaction (tick) granularity using incremental hashes. At each tick, the hash generates a signature of the entire state, which can be efficiently updated and compared. We evaluate the proposed signature scheme with a FIR filter and a Vorbis decoder and show that very fine-grain state consistency checking is feasible. The hash signature checking increases execution time of Bluespec RTL simulation by 1.2% for the FIR filter and by 2.2% for the Verbis decoder while correctly detecting any injected state inconsistency.

Published in:

Computer Design, 2007. ICCD 2007. 25th International Conference on

Date of Conference:

7-10 Oct. 2007