Profile-directed speculative optimization of reconfigurable floating point data paths

This paper presents a methodology for generating floatingpoint arithmetic hardware designs which are, for suitable applications, dramatically reduced in size, while still retaining performance. We use a profiling tool for floating-point value ranges to identify arithmetic operations where the shifting required for alignment and normalisation is almost always small. We synthesise hardware with reduced-size barrelshifters, but always detect when operands lie outside the range this optimised hardware can handle. These rare out-of-range operations are handled by a separate full floating-point implementation, either on-chip or by returning calculations to the host. Thus the system suffers no compromise in IEEE754 compliance. This paper presents results for two benchmark applications which profiling suggested would be profitable. We demonstrate the potential for this technique to yield an increase in parallel computing power of up to 43%, with a (correctable) error rate of less than 5%.