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Recurrent Neural Networks (RNNs) have the ability to retain memory and learn from data sequences, which are fundamental for real-time applications. RNN computations offer limited data reuse, which leads to high data traffic. This translates into high off-chip memory bandwidth or large internal storage requirement to achieve high performance. Exploiting parallelism in RNN computations are bounded by...
Previous approaches for utilizing automatic test equipment (ATE) vector repeat are based on identifying runs of repeated scan data and directly generating that data using ATE vector repeat. Each run requires a separate vector repeat instruction, so the amount of compression is limited by the amount of ATE instruction memory available and the length of the runs (which typically will be much shorter...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.