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Compilers for accelerator design languages (ADLs) translate high-level languages into application-specific hardware. ADL compilers rely on a hardwarecontrol interfaceto compose hardware units. There are two choices:staticcontrol, which relies on cycle-level timing; ordynamiccontrol, which uses explicit signalling to avoid depending on timing details. Static control is efficient but brittle; dynamic control incurs hardware costs to support compositional reasoning. Piezo is an ADL compiler that unifies static and dynamic control in a single intermediate language (IL). Its key insight is that the IL’s static fragment is arefinementof its dynamic fragment: static code admits a subset of the run-time behaviors of the dynamic equivalent. Piezo can optimize code by combining facts from static and dynamic submodules, and it opportunistically converts code from dynamic to static control styles. We implement Piezo as an extension to an existing dynamic ADL compiler, Calyx. We use Piezo to implement a frontend for an existing ADL, a systolic array generator, and a packet-scheduling hardware generator to demonstrate its optimizations and the static–dynamic interactions it enables. 

Where provenance is a relationship between a data item and the location from which this data was copied. In a DBMS, a typical use of where provenance is in establishing a copy-by-address relationship between the output of a query and the particular data value(s) that originated it. Normal DBMS operations create a variety of auxiliary copies of the data (e.g., indexes, MVs, cached copies). These copies exist over time with relationships that evolve continuously – (A) indexes maintain the copy with a reference to the origin value, (B) MVs maintain the copy without a reference to the source table, (C) cached copies are created once and are never maintained. A query may be answered from any of these auxiliary copies; however, this where provenance is not computed or maintained. In this paper, we describe sources from which forensic analysis of storage can derive where provenance of table data. We also argue that this computed where provenance can be useful (and perhaps necessary) for accurate forensic reports and evidence from maliciously altered databases or validation of corrupted DBMS storage.