The synthesis of application-specific machines using the Euler language

A rapid prototyping environment, called SAMUEL, for creating custom computing machines is described. The custom computing machines are synthesized by a compiler from a general purpose algorithmic language and a library of Verilog opcode circuits. The opcode circuits implement the interpretation rules defined for the algorithmic language. The compiler produces as output a Verilog description of the custom computing machine. This description can be used for simulation, or for synthesis with commercial tools;The opcode library makes SAMUEL unique among other research work that has been documented by raising the semantic level of the level 0 circuits. SAMUEL is also unique because the algorithmic language used is not a hardware description language, and it has not been modified in any way from the original language definition. Finally, SAMUEL is unique because the language chosen supports dynamic procedure definition. This allows a procedure to transform into a completely different procedure at runtime. This is language-supported reconfigurability which enhances the current research trends in reconfigurable devices;Custom computing machines generated by SAMUEL can be described using the scheme given by Milutinovic as software translated, language corresponding, complex, directly executing architectural support for the high-level language Euler (1). The approach differs from other work, however, by exploiting the field programmability of gate arrays (and the freedom guaranteed by a simulation environment) to create custom computing machines that only support the required language opcodes. This is important when the limited real-estate space of programmable logic is considered. Averaged real-estate savings can be achieved by not implementing support for the entire language on every custom computing machine

Finding Unexpected Events in Staring Continuous-Dwell Sensor Data Streams Via Adaptive Prediction

This research produced a Predictive Anomaly Detector (PAD). It is an adaptive prediction-based approach to detecting unexpected events in data streams drawn from staring continuous-dwell sensors. The underlying technology is spectrum independent and does not depend on correlated data (neither temporal nor spatial) to achieve improved detection and extraction in highly robust environments. ( robust environment refers to the data stream\u27s control law being variable and the spectral content covering a wide range of wavelengths.) The resulting approach uses a network of simple building-block equations (basis functions) to predict the non-event data and thereby present subtle sub-streams to a detection model as potential events of interest. The prediction model is automatically created from sequential observations of the data stream. Once model construction is complete, it continues to evolve as new samples arrive. Each sample value that is sufficiently different from the model\u27s predicted value is postulated as an unexpected event. A subsequent detection model uses a set of rules to confirm unexpected events while ignoring outliers. Intruder detection in robust video scenes is the main focus, although one demonstration achieved voice detection in a noisy audio signal. These demonstrations are coupled to a concept of operations that emphasizes the spectrum-independence of this approach and its integration with other processing requirements such as target recognition and tracking. Primary benefits delivered by this work include the ability to process large data volumes for obscured or buried information within highly active environments. The fully automated nature of this technique helps mitigate manning shortfalls typically associated with sorting through large volumes of surveillance data using trained analysts. This approach enables an organization to perform automated cueing for these analysts so that they spend less time examining data where nothing of interest exists. This maximizes the value of skilled personnel by using them to assess data with true potential. In this way, larger data volumes can be processed in a shorter period of time leading to a higher likelihood that important events and signals will be found, analyzed, and acted upon