  Viterbi decoders are widely used in communication systems, natural language
processing (NLP), and other domains. While Viterbi decoders are
compute-intensive and power-hungry, we can exploit approximations for early
design space exploration (DSE) of trade-offs between accuracy, power, and area.
We present Locate, a DSE framework that uses approximate adders in the
critically compute and power-intensive Add-Compare-Select Unit (ACSU) of the
Viterbi decoder. We demonstrate the utility of Locate for early DSE of
accuracy-power-area trade-offs for two applications: communication systems and
NLP, showing a range of pareto-optimal design configurations. For instance, in
the communication system, using an approximate adder, we observe savings of
21.5% area and 31.02% power with only 0.142% loss in accuracy averaged across
three modulation schemes. Similarly, for a Parts-of-Speech Tagger in an NLP
setting, out of 15 approximate adders, 7 report 100% accuracy while saving
22.75% area and 28.79% power on average when compared to using a
Carry-Lookahead Adder in the ACSU. These results show that Locate can be used
synergistically with other optimization techniques to improve the end-to-end
efficiency of Viterbi decoders for various application domains.
