Timed causal fanin analysis for symbolic simulation.

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Title: Timed causal fanin analysis for symbolic simulation.
Authors: Kaivola, Roope1 (AUTHOR) roope.k.kaivola@intel.com, Kama, Neta Bar2 (AUTHOR) neta.bar.kama@intel.com
Source: Formal Methods in System Design. Oct2025, Vol. 67 Issue 1, p3-26. 24p.
Subjects: Intel microprocessors, Formal methods (Computer science), Intel Corp., Computer circuits
Abstract: Symbolic circuit simulation has been the main vehicle for formal verification of Intel Core processor execution engines for over twenty years. It extends traditional simulation by allowing symbolic variables in the stimulus, covering the circuit behavior for all possible values simultaneously. A distinguishing feature of symbolic simulation is that it gives the human verifier clear visibility into the progress of the computation during the verification of an individual operation, and fine-grained control over the simulation to focus only on the datapath for that operation while abstracting away the rest of the circuit behavior. In this paper we describe an automated simulation complexity reduction method called timed causal fanin analysis that can be used to carve out the minimal circuit logic needed for verification of an operation on a cycle-by-cycle basis. The method has been a key component of Intel's large-scale execution engine verification efforts, enabling closed-box verification of most operations in the interface level. As a specific application, we discuss the formal verification of Intel's new half-precision floating-point FP16 micro-instruction set. Thanks to the ability of the timed causal fanin analysis to separate the half-precision datapaths from full-width ones, we were able to verify all these instructions closed box, including the most complex ones like fused multiply-add and division. This led to early detection of several deep datapath bugs. [ABSTRACT FROM AUTHOR]
Copyright of Formal Methods in System Design is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Symbolic circuit simulation has been the main vehicle for formal verification of Intel Core processor execution engines for over twenty years. It extends traditional simulation by allowing symbolic variables in the stimulus, covering the circuit behavior for all possible values simultaneously. A distinguishing feature of symbolic simulation is that it gives the human verifier clear visibility into the progress of the computation during the verification of an individual operation, and fine-grained control over the simulation to focus only on the datapath for that operation while abstracting away the rest of the circuit behavior. In this paper we describe an automated simulation complexity reduction method called timed causal fanin analysis that can be used to carve out the minimal circuit logic needed for verification of an operation on a cycle-by-cycle basis. The method has been a key component of Intel's large-scale execution engine verification efforts, enabling closed-box verification of most operations in the interface level. As a specific application, we discuss the formal verification of Intel's new half-precision floating-point FP16 micro-instruction set. Thanks to the ability of the timed causal fanin analysis to separate the half-precision datapaths from full-width ones, we were able to verify all these instructions closed box, including the most complex ones like fused multiply-add and division. This led to early detection of several deep datapath bugs. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Formal Methods in System Design is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1007/s10703-025-00478-1
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              Text: Oct2025
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