401 research outputs found
Optimization and Abstraction: A Synergistic Approach for Analyzing Neural Network Robustness
In recent years, the notion of local robustness (or robustness for short) has
emerged as a desirable property of deep neural networks. Intuitively,
robustness means that small perturbations to an input do not cause the network
to perform misclassifications. In this paper, we present a novel algorithm for
verifying robustness properties of neural networks. Our method synergistically
combines gradient-based optimization methods for counterexample search with
abstraction-based proof search to obtain a sound and ({\delta}-)complete
decision procedure. Our method also employs a data-driven approach to learn a
verification policy that guides abstract interpretation during proof search. We
have implemented the proposed approach in a tool called Charon and
experimentally evaluated it on hundreds of benchmarks. Our experiments show
that the proposed approach significantly outperforms three state-of-the-art
tools, namely AI^2 , Reluplex, and Reluval
Cerberus-BMC: A Principled Reference Semantics and Exploration Tool for Concurrent and Sequential C
C remains central to our infrastructure, making verification of C code
an essential and much-researched topic, but the semantics of C is
remarkably complex, and important aspects of it are still unsettled,
leaving programmers and verification tool builders on shaky
ground.
This paper describes a tool, Cerberus-BMC, that for the first time
provides a principled reference semantics that simultaneously supports
(1) a choice of concurrency memory model (including substantial
fragments of the C11, RC11, and Linux kernel memory models), (2) a modern memory object model,
and (3) a well-validated thread-local semantics for a large fragment
of the language.
The tool should be useful for C programmers, compiler writers,
verification tool builders, and members of the C/C++ standards committees
ImageEye: Batch Image Processing Using Program Synthesis
This paper presents a new synthesis-based approach for batch image
processing. Unlike existing tools that can only apply global edits to the
entire image, our method can apply fine-grained edits to individual objects
within the image. For example, our method can selectively blur or crop specific
objects that have a certain property. To facilitate such fine-grained image
editing tasks, we propose a neuro-symbolic domain-specific language (DSL) that
combines pre-trained neural networks for image classification with other
language constructs that enable symbolic reasoning. Our method can
automatically learn programs in this DSL from user demonstrations by utilizing
a novel synthesis algorithm. We have implemented the proposed technique in a
tool called ImageEye and evaluated it on 50 image editing tasks. Our evaluation
shows that ImageEye is able to automate 96% of these tasks
goSLP: Globally Optimized Superword Level Parallelism Framework
Modern microprocessors are equipped with single instruction multiple data
(SIMD) or vector instruction sets which allow compilers to exploit superword
level parallelism (SLP), a type of fine-grained parallelism. Current SLP
auto-vectorization techniques use heuristics to discover vectorization
opportunities in high-level language code. These heuristics are fragile, local
and typically only present one vectorization strategy that is either accepted
or rejected by a cost model. We present goSLP, a novel SLP auto-vectorization
framework which solves the statement packing problem in a pairwise optimal
manner. Using an integer linear programming (ILP) solver, goSLP searches the
entire space of statement packing opportunities for a whole function at a time,
while limiting total compilation time to a few minutes. Furthermore, goSLP
optimally solves the vector permutation selection problem using dynamic
programming. We implemented goSLP in the LLVM compiler infrastructure,
achieving a geometric mean speedup of 7.58% on SPEC2017fp, 2.42% on SPEC2006fp
and 4.07% on NAS benchmarks compared to LLVM's existing SLP auto-vectorizer.Comment: Published at OOPSLA 201
Inductive Program Synthesis via Iterative Forward-Backward Abstract Interpretation
A key challenge in example-based program synthesis is the gigantic search
space of programs. To address this challenge, various work proposed to use
abstract interpretation to prune the search space. However, most of existing
approaches have focused only on forward abstract interpretation, and thus
cannot fully exploit the power of abstract interpretation. In this paper, we
propose a novel approach to inductive program synthesis via iterative
forward-backward abstract interpretation. The forward abstract interpretation
computes possible outputs of a program given inputs, while the backward
abstract interpretation computes possible inputs of a program given outputs. By
iteratively performing the two abstract interpretations in an alternating
fashion, we can effectively determine if any completion of each partial program
as a candidate can satisfy the input-output examples. We apply our approach to
a standard formulation, syntax-guided synthesis (SyGuS), thereby supporting a
wide range of inductive synthesis tasks. We have implemented our approach and
evaluated it on a set of benchmarks from the prior work. The experimental
results show that our approach significantly outperforms the state-of-the-art
approaches thanks to the sophisticated abstract interpretation techniques
- …