Cisco Technology, Inc.

Christopher H Pham

Jun Xu

Xiangrong Wang

Srinivas Goli

A programmable logic unit (e.g., an ASIC or FPGA) having a feedforward linear associative memory (LAM) neural network checking circuit which classifies input vectors to a faulty hardware block as either good or not good and, when a new input vector is classified as not good, blocks a corresponding output vector of the faulty hardware block, enables a software work-around for the new input vector, and accepts the software work-around input as the output vector of the programmable logic circuit. The feedforward LAM neural network checking circuit has a weight matrix whose elements are based on a set of known bad input vectors for said faulty hardware block. The feedforward LAM neural network checking circuit may update the weight matrix online using one or more additional bad input vectors. A discrete Hopfield algorithm is used to calculate the weight matrix W. The feedforward LAM neural network checking circuit calculates an output vector aby multiplying the weight matrix W by the new input vector b, that is, a=Wb, adjusts elements of the output vector aby respective thresholds, and processes the elements using a plurality of non-linear units to provide an output of 1 when a given adjusted element is positive, and provide an output of 0 when a given adjusted element is not positive. If a vector constructed of the outputs of these non-linear units matches with an entry in a content-addressable memory (CAM) storing the set of known bad vectors (a CAM hit), then the new input vector is classified as not good.

Linear associative memory-based hardware architecture for fault tolerant ASIC/FPGA work-around

Systems, methods, apparatus and software can be implemented to provide a debugger agent, either separate form or integrated into the script-based testing platform, to coordinate the selection, activation, and or/operation of debuggers suitable for a particular unit and program under test. Such a debugger agent can provide run time monitoring and debugging activities where previously not possible. The debugger agent is generally independent agent, in that it can work with a variety of types of test scripts, test script handlers, programming languages, and debuggers without customization. Moreover, implementation generally needs no code instrumentation.

Source code debugging method and apparatus for use in script testing environment

Systems, methods, apparatus and software can be implemented to detect memory leaks with relatively high confidence. By analyzing memory blocks stored in a memory, implicit and/or explicit contingency chains can be obtained. Analysis of these contingency chains identifies potential memory leaks, and subsequent verification confirms whether the potential memory leaks are memory leaks.

Memory leak detection system and method using contingency analysis

A programmable logic unit (e.g., an ASIC or FPGA) having a feedforward linear associative memory (LAM) neural network checking circuit which classifies input vectors to a faulty hardware block as either good or not good and, when a new input vector is classified as not good, blocks a corresponding output vector of the faulty hardware block, enables a software work-around for the new input vector, and accepts the software work-around input as the output vector of the programmable logic circuit. The feedforward LAM neural network checking circuit has a weight matrix whose elements are based on a set of known bad input vectors for said faulty hardware block. The feedforward LAM neural network checking circuit may update the weight matrix online using one or more additional bad input vectors. A discrete Hopfield algorithm is used to calculate the weight matrix W. The feedforward LAM neural network checking circuit calculates an output vector aby multiplying the weight matrix W by the new input vector b, that is, a=wb, adjusts elements of the output vector aby respective thresholds, and processes the elements using a plurality of non-linear units to provide an output of 1 when a given adjusted element is positive, and provide an output of 0 when a given adjusted element is not positive. If a vector constructed of the outputs of these non-linear units matches with an entry in a content-addressable memory (CAM) storing the set of known bad vectors (a CAM hit), then the new input vector is classified as not good.

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Milpitas, CA, United States of America

Christopher H Pham