Abstract:
An automated method of making a prediction regarding development of a software product includes receiving code changes information, build information, and failure information related to the software product. Entries are stored in a database, wherein each entry links a subset of the code changes information with a subset of the build information and with a subset of the failure information. A first matrix and a second matrix are generated using the entries in the database. Multi-target entropy calculations are performed based on the first matrix and the second matrix. The prediction regarding the development of the software product is performed based on the multi-target entropy calculations.
Abstract:
An example electronic device includes a diagnostic interface to establish a connection with a diagnostic device via a host cable. The electronic device also includes a detection circuit coupled to the diagnostic interface. The electronic device further includes a protocol conversion circuit coupled to the detection circuit to convert a transmission protocol of data received via the diagnostic interface. The electronic device further includes an electronic component coupled to the detection circuit and coupled to the protocol conversion circuit. The detection circuit is to determine a connection type of the connection based on a detection of a power signal asserted to the diagnostic interface via the host cable and to select, based on the connection type, a data communication path between the electronic component and the diagnostic interface to route a diagnostic command.
Abstract:
A method and apparatus for detecting and mitigating faults in numerical computations of M input data streams is claimed (embodiments of Figure 1 and Figure 14). Such faults may occur due to circuit or processor malfunctions stemming from (but not limited to): supply voltage or current fluctuation, timing signal errors, hardware device noise, or other signalling, hardware, or software non-idealities. The invented method and apparatus for numerical entanglement linearly superimposes M input data streams to form M numerically-entangled data streams that can optionally be stored in-place of the original inputs (as in the example embodiments of: Step 2 of Figure 1 and item 1054 of Figure 14). A series of operations, such as (but not limited to): scaling, additions/subtractions, inner or outer vector or matrix products and permutations, can then be performed directly using these entangled data streams (as in the example embodiment of Step 3 of Figure 1, operator g of Figure 2, Figures 6-11, item 1053 of Figure 14). The output results are disentangled from the M entangled output streams by additions and arithmetic shifts (example embodiments of Steps 4 and 5 of Figure 1, "disentanglement and fault checking" of Figure 2, item 1056 of Figure 14). A post-computation reliability check detects processing errors affecting disentangled outputs (example embodiments of item 1056 of Figure 14, Figures 15a, 15b, 16a, 16b, 17a, 17b).
Abstract:
집적 회로 장치 및 상기 집적 회로 장치에서의 신호 처리 방법이 개시된다. 본 발명의 일 실시예에 따른 집적 회로 장치는, 외부 인터페이스로부터 인가되는 리셋 신호에 따라 초기화를 수행하고 상기 외부 인터페이스로 리셋 응답(ATR; Answer-To-Reset)을 송신하는 제1 처리부; 및 상기 제1 처리부의 초기화 과정에서 상기 집적 회로 장치가 셧다운 상태인 것으로 판단되는 경우, 상기 집적 회로 장치에 대한 진단 데이터를 상기 외부 인터페이스로 송신하는 제2 처리부를 포함한다.
Abstract:
A system includes a processor and a memory system in communication with the processor. The memory system stores instructions that when executed by the processor result in the system being operable to access an event list that defines a plurality of events and a scenario list that defines a plurality of scenarios as routes through a tree structure that includes one or more of the events for each of the scenarios. The system is also operable to build a specific risk matrix that calculates a plurality of combined probabilities based on each pairing of an occurrence of each of the events in combination with each of the scenarios. The system is further operable to output a residual probability for each of the events based on a summation of the combined probabilities for each of the events.