{"content":[{"text":"Constrained Sequence Generation Using Stochastic \n Sequential Machines \n Diana Marculescu, Radu Marculescu, Massoud Pedram \n Department of Electrical Engineering-Systems \n University of Southern California, Los Angeles, CA 90089","bbox":{"left":0.178105,"top":0.082333,"width":0.643794,"height":0.144011}},{"text":"Abstract","bbox":{"left":0.457435,"top":0.279942,"width":0.08512100000000006,"height":0.024482000000000004}},{"text":"In power estimation, one is faced with two problems: 1) generating input vector sequences that \n satisfy a given statistical behavior (in terms of signal probabilities and correlations among \n bits); 2) making these sequences as short as possible so as to improve the efficiency of power \n simulators. Stochastic sequential machines (SSMs) can be used to solve both problems. In \n particular, this paper presents a general procedure for SSM synthesis and describes a new \n framework for sequence characterization to match designer’s needs for sequence generation or \n compaction. Experimental results demonstrate that compaction ratios of 1-3 orders of \n magnitude can be obtained without much loss in accuracy of total power estimates.","bbox":{"left":0.117647,"top":0.327763,"width":0.764834,"height":0.2041059999999999}},{"text":"2","bbox":{"left":0.495915,"top":0.94125,"width":0.00817000000000001,"height":0.016843000000000052}}],"images_bbox":[],"images_bbox_not_text_overlap":[]}