Fine grain word length optimization for dynamic precision scaling in DSP systems

Dynamic precision scaling is a promising technique to reduce power consumption in Digital Signal Processing (DSP) systems. Power savings are achieved by dynamically adapting word lengths to a time-varying environment. Typical applications are wireless communication systems that operate under different wireless channel conditions. One of the obstacles of such techniques is that they require an optimization of word lengths for all intermediate values at all possible operation points. This makes traditional simulation-based fixed-point optimization infeasible. In this paper, we study efficient heuristics to find optimal sets of word lengths for all variables in a system under a range of operating conditions. We exploit statistical analysis of quantization noise coupled with Additive White Gaussian Noise (AWGN) models of the channel environment. Applied to an example of a Fast Fourier Transform (FFT) block in an Orthogonal Frequency Division Multiplexing (OFDM) system, a more than 5,000x improvement in optimization time compared to an efficient simulation-based word length optimization method can be achieved.