Recent high-level synthesis approaches and C-based hardware description languages attempt to improve the hardware design process by allowing developers to capture desired hardware functionality in a well-known high-level source language. However, these approaches have yet to achieve wide commercial success due in part to the difficulty of incorporating such approaches into software tool flows. The requirement of using a specific language, compiler, or development environment may cause many software developers to resist such approaches due to the difficulty and possible instability of changing well-established robust tool flows. Thus, in the past several years, synthesis from binaries has been introduced, both in research and in commercial tools, as a means of better integrating with tool flows by supporting all high-level languages and software compilers. Binary synthesis can be more easily integrated into a software development tool-flow by only requiring an additional backend tool, and it even enables completely transparent dynamic translation of executing binaries to configurable hardware circuits. In this article, we survey the key technologies underlying the important emerging field of binary synthesis. We compare binary synthesis to several related areas of research, and we then describe the key technologies required for effective binary synthesis: decompilation techniques necessary for binary synthesis to achieve results competitive with source-level synthesis, hardware/software partitioning methods necessary to find critical binary regions suitable for synthesis, synthesis methods for converting regions to custom circuits, and binary update methods that enable replacement of critical binary regions by circuits.

Multi-spectral satellite image sets present a storage and transmission problem. These image sets can also contain line features that typical detection methods do not resolve sharply enough for some purposes. The Karhunen-Loève transform (KLT) presents a solution to both these problems. Firstly, the KLT can be used as a form of lossy data compression, only retaining the higherorder images in the transformed set, as these contain the significant features. The KLT can also substitute as a coder of wavelet transform coefficients, equally for the purposes of compression. Secondly, when an image contains buildings then one technique is to use a KLT in order to highlight these features. The KLT can also provide a characteristic signature of differing regions in multi-spectral imagery and unlike 2D methods prior image fusion is not required. However, the KLT’s kernel is data-dependent unlike related orthogonal transforms. In other words, it has to be re-calculated for each image set to which it is applied. This causes a computational bottleneck when batches of satellite images are processed. Neither conventional uniprocessors or medium-grained parallel machines are well-matched to KLT processing, either calculation of the kernel or subsequent application of the transform to the image set data. This is