1/13/2024 0 Comments Precise scaling in birdfontThe maximum number of bits for any operation of an application should be enough to support worst-case conditions, but some of the lower bits can remain inactive for different operating points. Precision scaling can be realized by bit-level clock gating of the least significant bits as shown in Fig. Such dynamic precision scaling mostly targets application-specific hardware implementations , although software implementations also exist . In more aggressive recent power saving methods, a system’s precision is dynamically and adaptively configured in reaction to changing operating points while maintaining a certain signal quality level . Traditionally, fixed-point word lengths of custom hardware or software implementations are set to match worst-case operating conditions determined at design time. Scaling internal system precision continues to be one of the most important mechanisms to reduce implementation complexity and hence improve performance and power consumption in digital signal processing (DSP) systems. Our results show a more than 5,000x improvement in optimization time compared to an efficient simulation-based word length optimization method with less than 10 % estimation error across a range of target quality metrics. We apply our method to several industrial-strength examples. In this chapter, we study efficient analytical heuristics to find optimal sets of word lengths for all variables and operations in a dataflow graph constrained by mean squared error type of metrics. One of the obstacles for adoption of fine-grain scaling techniques is that they require determining the precision of all intermediate values at all possible operation points, making simulation-based optimization infeasible. Recently, this topic has been explored in a broader approximate computing context, where architectures allowing for fine-grain control of hardware or software accuracy have been proposed. Finding optimal word lengths in digital signal processing systems has been one of the primary mechanisms for reducing complexity.
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