While general purpose processors reach both high performance and high application flexibility, this comes at a high cost in terms of silicon area and power consumption. In systems where high application flexibility is not required, it is possible to trade off flexibility for lower cost by tailoring the processor to the application to create an Application Specific Instruction set Processor (ASIP) with high performance yet low silicon cost.

This thesis demonstrates how ASIPs with application specific data types can provide efficient solutions with lower cost. Two examples are presented, an audio decoder ASIP for audio and music processing and a matrix manipulation ASIP for MIMO radio baseband signal processing.

The audio decoder ASIP uses a 16-bit floating point data type to reduce the size of the data memory to about 60% of other solutions that use a 32-bit data type. Since the data memory occupies a major part of the silicon area, this has a significant impact on the total silicon area, and thereby also the static and dynamic power consumption. The data width reduction can be done without any noticeable artifacts in the decoded audio due to the natural masking effect ofthe human ear.

The matrix manipulation SIMD ASIP is designed to perform various matrix operations such as matrix inversion and QR decomposition of small complex-valued matrices. This type of processing is found in MIMO radio baseband signal processing and the matrices are typically not larger than 4x4. There have been solutions published that use arrays of fixed-function processing elements to perform these operations, but the proposed ASIP performs the computations in less time and with lower hardware cost.

The matrix manipulation ASIP data path uses a floating point data type to avoid data scaling issues associated with fixed point computations, especially those related to division and reciprocal calculations, and it also simplifies the program control flow since no special cases for certain inputs are needed which is especially important for SIMD architectures.

These two applications were chosen to show how ASIPs can be a suitable alternative and match the requirements for different types of applications, to provide enough flexibility and performance to support different standards and algorithms with low hardware cost.