We analyze reported patches for three prior generate-andvalidate patch generation systems (GenProg, RSRepair, and AE). Because of experimental error, the majority of the reported patches violate the basic principle behind the design of these systems -they do not produce correct outputs even for the inputs in the test suite used to validate the patches. We also show that the overwhelming majority of the accepted patches are not correct and are equivalent to a single modification that simply deletes functionality.We also present Kali, a generate-and-validate patch generation system that simply deletes functionality. Working with a simpler and more effectively focused search space, Kali generates at least as many correct patches as prior GenProg, RSRepair, and AE systems. Kali also generates at least as many plausible patches that produce correct outputs for the inputs in the validation test suite as the three prior systems.We also discuss the patches produced by ClearView, a generate-and-validate binary hot patching system that leverages learned invariants to produce patches that enable systems to survive otherwise fatal defects and security attacks.

The accuracy of an approximate computation is the distance between the result that the computation produces and the corresponding fully accurate result. The reliability of the computation is the probability that it will produce an acceptably accurate result. Emerging approximate hardware platforms provide approximate operations that, in return for reduced energy consumption and/or increased performance, exhibit reduced reliability and/or accuracy.We present Chisel, a system for reliability-and accuracyaware optimization of approximate computational kernels that run on approximate hardware platforms. Given a combined reliability and/or accuracy specification, Chisel automatically selects approximate kernel operations to synthesize an approximate computation that minimizes energy consumption while satisfying its reliability and accuracy specification.We evaluate Chisel on five applications from the image processing, scientific computing, and financial analysis domains. The experimental results show that our implemented optimization algorithm enables Chisel to optimize our set of benchmark kernels to obtain energy savings from 8.7% to 19.8% compared to the original (exact) kernel implementations while preserving important reliability guarantees.

We present Topaz, a new task-based language for computations that execute on approximate computing platforms that may occasionally produce arbitrarily inaccurate results. Topaz maps tasks onto the approximate hardware and integrates the generated results into the main computation. To prevent unacceptably inaccurate task results from corrupting the main computation, Topaz deploys a novel outlier detection mechanism that recognizes and precisely reexecutes outlier tasks. Outlier detection enables Topaz to work effectively with approximate hardware platforms that have complex fault characteristics, including platforms with bit pattern dependent faults (in which the presence of faults may depend on values stored in adjacent memory cells). Our experimental results show that, for our set of benchmark applications, outlier detection enables Topaz to deliver acceptably accurate results (less than 1% error) on our target approximate hardware platforms. Depending on the application and the hardware platform, the overall energy savings range from 5 to 13 percent. Without outlier detection, only one of the applications produces acceptably accurate results.

The accuracy of an approximate computation is the distance between the result that the computation produces and the corresponding fully accurate result. The reliability of the computation is the probability that it will produce an acceptably accurate result. Emerging approximate hardware platforms provide approximate operations that, in return for reduced energy consumption and/or increased performance, exhibit reduced reliability and/or accuracy.We present Chisel, a system for reliability-and accuracyaware optimization of approximate computational kernels that run on approximate hardware platforms. Given a combined reliability and/or accuracy specification, Chisel automatically selects approximate kernel operations to synthesize an approximate computation that minimizes energy consumption while satisfying its reliability and accuracy specification.We evaluate Chisel on five applications from the image processing, scientific computing, and financial analysis domains. The experimental results show that our implemented optimization algorithm enables Chisel to optimize our set of benchmark kernels to obtain energy savings from 8.7% to 19.8% compared to the original (exact) kernel implementations while preserving important reliability guarantees.

Programmable analog devices have emerged as a powerful computing substrate for performing complex neuromorphic and cytomorphic computations. We present Arco, a new solver that, given a dynamical system specification in the form of a set of differential equations, generates physically realizable configurations for programmable analog devices that are algebraically equivalent to the specified system. On a set of benchmarks from the biological domain, Arco generates configurations with 35 to 534 connections and 28 to 326 components in 1 to 54 minutes.