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Smart Cameras as Embedded Systems
Recent technological advances are enabling a new generation of smart cameras that represent a quantum leap in sophistication. While today's digital cameras capture images, smart cameras capture high-level descriptions of the scene and analyze what they see. These devices could support a wide variety of applications including human and animal detection, surveillance, motion analysis, and facial identification. Video processing has an insatiable demand for real-time performance. Smart cameras leverage very large-scale integration to meet this need in a low-cost, low-power system with substantial memory. Moving well beyond pixel processing and compression, these VLSI systems run a wide range of algorithms to extract meaning from streaming video. Recently, Princeton University researchers developed a first-generation smart camera system that can detect people and analyze their movement in real time. Because they push the design space in so many dimensions, these smart cameras are a leading-edge application for embedded system research.
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Code compression for embedded systems
Memory is one of the most restricted resources in many modern embedded systems. Code compression can provide substantial savings in terms of size. In a compressed code CPU, a cache miss triggers the decompression of a main memory block, before it gets transferred to the cache. Because the code must be decompressible starting from any point (or at least at cache block boundaries), most file-oriented compression techniques cannot be used. We propose two algorithms to compress code in a space-efficient and simple to decompress way, one which is independent of the instruction set and another which depends on the instruction set. We perform experiments on true instruction sets, a typical RISC (MIPS) and a typical CISC (x86) and compare our results to existing file-oriented compression algorithms.
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Guest Editorial