77,841 research outputs found
Symbolic QED Pre-silicon Verification for Automotive Microcontroller Cores: Industrial Case Study
We present an industrial case study that demonstrates the practicality and
effectiveness of Symbolic Quick Error Detection (Symbolic QED) in detecting
logic design flaws (logic bugs) during pre-silicon verification. Our study
focuses on several microcontroller core designs (~1,800 flip-flops, ~70,000
logic gates) that have been extensively verified using an industrial
verification flow and used for various commercial automotive products. The
results of our study are as follows: 1. Symbolic QED detected all logic bugs in
the designs that were detected by the industrial verification flow (which
includes various flavors of simulation-based verification and formal
verification). 2. Symbolic QED detected additional logic bugs that were not
recorded as detected by the industrial verification flow. (These additional
bugs were also perhaps detected by the industrial verification flow.) 3.
Symbolic QED enables significant design productivity improvements: (a) 8X
improved (i.e., reduced) verification effort for a new design (8 person-weeks
for Symbolic QED vs. 17 person-months using the industrial verification flow).
(b) 60X improved verification effort for subsequent designs (2 person-days for
Symbolic QED vs. 4-7 person-months using the industrial verification flow). (c)
Quick bug detection (runtime of 20 seconds or less), together with short
counterexamples (10 or fewer instructions) for quick debug, using Symbolic QED
Incremental Entity Resolution from Linked Documents
In many government applications we often find that information about
entities, such as persons, are available in disparate data sources such as
passports, driving licences, bank accounts, and income tax records. Similar
scenarios are commonplace in large enterprises having multiple customer,
supplier, or partner databases. Each data source maintains different aspects of
an entity, and resolving entities based on these attributes is a well-studied
problem. However, in many cases documents in one source reference those in
others; e.g., a person may provide his driving-licence number while applying
for a passport, or vice-versa. These links define relationships between
documents of the same entity (as opposed to inter-entity relationships, which
are also often used for resolution). In this paper we describe an algorithm to
cluster documents that are highly likely to belong to the same entity by
exploiting inter-document references in addition to attribute similarity. Our
technique uses a combination of iterative graph-traversal, locality-sensitive
hashing, iterative match-merge, and graph-clustering to discover unique
entities based on a document corpus. A unique feature of our technique is that
new sets of documents can be added incrementally while having to re-resolve
only a small subset of a previously resolved entity-document collection. We
present performance and quality results on two data-sets: a real-world database
of companies and a large synthetically generated `population' database. We also
demonstrate benefit of using inter-document references for clustering in the
form of enhanced recall of documents for resolution.Comment: 15 pages, 8 figures, patented wor
DIP: Disruption-Tolerance for IP
Disruption Tolerant Networks (DTN) have been a popular subject of recent
research and development. These networks are characterized by frequent, lengthy
outages and a lack of contemporaneous end-to-end paths. In this work we discuss
techniques for extending IP to operate more effectively in DTN scenarios. Our
scheme, Disruption Tolerant IP (DIP) uses existing IP packet headers, uses the
existing socket API for applications, is compatible with IPsec, and uses
familiar Policy-Based Routing techniques for network management
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