Digital Signal Processing (DSP) technology is one of the more recent developments of the fast evolving electronics technology and yet it is increasingly used in daily life. Celullar telephones, modems, CD-ROM devices, audio equipment, hard disks and automobile electronics are examples of the broad spectrum of the broad application spectrum of DSP devices . The first programmable DSP processors appeared in the early 80's and were intended as audio processors. Within ten years the processing power of custom digitial signal processing hardware has became capable of processing video rate signals. The development of multimedia applications and standarts led to another development where recently general purpose processors started including specialized hardware to acquire basic signal processing capability.
Even the most simple DSP algorithms require extensive computation and DSP operations in general are still considered to be among the most demanding operations in integrated circuit technology. A typical DSP operation consists of a series of multiplications on stored values of input data with a set of co-efficients, producing results which are then accumulated. As the signal has to be processed continously, the bandwidth of the signal puts strict constraints on the speed of operation. As an example, for video applications 10 to 75 pictures need to be processed each second. Depending on the resolution each picture may have as many as two million pixels, where each pixel is represented by (at least) 8 bits. Any video processing algorithm will therefore need to calculate the result of the operation on the order of tens of nanoseconds.
High performance digital signal processing architectures rely on complicated designs, that are usually tailored for a particular application. Moreover, these designs are rather large and have transistor counts on the order of millions of transistors. These designs are usually parts of a custom chip-set for a specific application such as MPEG decoding. It is extremely hard to adapt these designs for new requirements as most of them have application oriented optimizations.
In this work, a programmable, fully pipelined compact LSI macro block (Aries) is presented, that can be used to design 1-D or 2-D convolutional filters of any size with a clock cycle of 20 ns. The design is extremely compact and occupies an active silicon area of less than 1.5mm^2 in a conventional 0.8um digital CMOS technology. Several Aries macro-blocks can therefore easily be embedded in a larger design. The fully pipelined 20 ns-cycle operation time is sufficient for high resolution image processing. Aries is fully programmable (at a lower speed than the data input rate), which gives it an adaptive filtering capability. Aries also includes the hardware necessary to accumulate the results of several Aries chips and can easily be scaled to accommodate digital filters of any dimensions.
There are three basic alternatives for the construction of digital filters . The commonly used two alternatives employ multipliers. In Aries there are no multipliers, the data is broken down into its bitplanes. The data-bits of the same order are grouped to form a RAM address, and a pre-computed partial result is read from a RAM. The partial results corresponding to these data-bits are then weighted and accumulated in an adder array to form a result.
In Chapter 2, a brief introduction to Digital Signal Processing is given,with an emphasis on the binary number systems used in digital systems. Aries is developed from earlier work on Taurus, A Capacitive Threshold Logic based image filter . Chapter 3 shortly describes the basic architecture of Taurus and discusses the main problems encountered during design. The design of Aries is explained in detail in Chapters 4 (general structure), Chapter 5 (RAM design), Chapter 6 (adder design) and Chapter 7 (Implementation). As a speed and area optimized design, Aries has been designed using a full-custom design methodology. Some aspects of this design methodology are explained using the design of one of the main blocks of Aries in Chapter 8. Finally, Chapter 9 summarizes all the results.