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In this paper, we propose a framework for low-energy digital signal processing (DSP), where the supply voltage is scaled beyond the critical voltage imposed by the requirement to match the critical path delay to the throughput. This deliberate introduction of input-dependent errors leads to degradation in the algorithmic performance, which is compensated for via algorithmic noise-tolerance (ANT) schemes. The resulting setup that comprises of the DSP architecture operating at subcritical voltage and the error control scheme is referred to as soft DSP. The effectiveness of the proposed scheme is enhanced when arithmetic units with a higher "delay imbalance" are employed. A prediction-based error-control scheme is proposed to enhance the performance of the filtering algorithm in the presence of errors due to soft computations. For a frequency selective filter, it is shown that the proposed scheme provides 60-81% reduction in energy dissipation for filter bandwidths up to 0.5 /spl pi/ (where 2 /spl pi/ corresponds to the sampling frequency f/sub s/) over that achieved via conventional architecture and voltage scaling, with a maximum of 0.5-dB degradation in the output signal-to-noise ratio (SNR/sub o/). It is also shown that the proposed algorithmic noise-tolerance schemes can also be used to improve the performance of DSP algorithms in presence of bit-error rates of up to 10/sup -3/ due to deep submicron (DSM) noise.