Amir Geva

An innovative low-power high-performance programmable signal processor for digital communications

We describe an innovative, low-power, high-performance, programmable signal processor (DSP) for digital communications. The architecture of this processor is characterized by its explicit design for low-power implementations, its innovative ability to jointly exploit instruction-level parallelism and data-level parallelism to achieve high performance, its suitability as a target for an optimizing high-level language compiler, and its explicit replacement of hardware resources by compile-time practices. We describe the methodology used in the development of the processor, highlighting the techniques deployed to enable application/architecture/compiler/implementation co-development, and the optimization approach and metric used for power-performance evaluation and tradeoff analysis. We summarize the salient features of the architecture, provide a brief description of the hardware organization, and discuss the compiler techniques used to exercise these features. We also summarize the simulation environment and associated software development tools. Coding examples from two representative kernels in the digital communications domain are also provided. The resulting methodology, architecture, and compiler represent an advance of the state of the art in the area of low-power, domain-specific microprocessors.

Detection and Segmentation of Antialiased Text in Screen Images

Various software applications deal with analyzing the textual content of screen captures. Interpreting these images as text poses several challenges, relative to images traditionally handled by optical character recognition (OCR) engines. One such challenge is caused by text antialiasing, a technique which blurs the edges of characters, to reduce jagged appearance. This blurring changes the character images according to context, and can sometimes fuse them together. In this paper, we offer a low-cost method that can be used as a preprocessing stage, prior to OCR. Our method locates antialiased text in a screen image and segments it into separate character images. Our proposed algorithm significantly improves OCR results, particularly in images with colored text of small font size, such as in graphic user interface (GUI) screens.

Lightweight searchable screen video recording

Command logging of maintenance and operation activities of modern computer systems has become an integral component of customer and audit requirements. In recent years, this logging has usually been achieved via desktop video recording. However, the conventional approach of video recording requires high computation overhead, high network bandwidth, and a large storage size. Searching through video files is also a challenge. In this paper, we present a lossy, but text text-preserving, compression scheme that meets these challenges by creating a sparse bitonal image suitable for optical character recognition (OCR). Using our system for auditing, the bitonal image gets stored on a server. Due to the mechanisms text-preserving compression, we can apply OCR off-line to create annotations of each video frame, making the output searchable. Compared to state-of-the-art compression of raw video, our approach can reduce file size by 50-80%, while using CPU and memory resources similar to other methods.