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Power Optimization and Synthesis at Behavioral and System by Jui-Ming Chang, Massoud Pedram

By Jui-Ming Chang, Massoud Pedram

Integrated circuit densities and working speeds proceed to upward thrust at an exponential cost. Chips, despite the fact that, can't get higher and quicker with no sharp reduce in strength intake past the present degrees. Minimization of energy intake in VLSI chips has therefore develop into a tremendous layout goal. in truth, with the explosive development renowned for transportable electronics and the standard push towards extra complicated performance and better functionality, strength intake has in lots of situations turn into the proscribing think about pleasurable the marketplace call for.
a brand new iteration of power-conscious CAD instruments are coming onto the marketplace to aid designers estimate, optimize and confirm energy intake degrees at so much phases of the IC layout method. those instruments are in particular normal on the register-transfer point and lower than. there's a nice want for related instruments and functions on the behavioral and procedure degrees of the layout technique. Many researchers and CAD software builders are engaged on high-level strength modeling and estimation, in addition to power-constrained high-level synthesis and optimization. recommendations and instruments on my own are, besides the fact that, inadequate to optimize VLSI circuit strength dissipation - a constant and convergent layout method can be required.
Power Optimization and Synthesis at Behavioral and process degrees utilizing Formal Methods was once written to handle a number of the key difficulties in strength research and optimization early within the layout procedure. particularly, this publication specializes in strength macro-modeling in keeping with regression research and tool minimization via behavioral adjustments, scheduling, source project and hardware/software partitioning and mapping. What differentiates this booklet from different released paintings at the topic is the mathematical foundation and formalism at the back of the algorithms and the optimality of those algorithms topic to the acknowledged assumptions.
From the Foreword:
`This booklet makes an immense contribution to the sector of method layout applied sciences through featuring a collection of algorithms with assured optimality houses, that may be with no trouble utilized to system-level layout. This contribution is well timed, since it fills the necessity of recent equipment for a brand new layout device new release, which helps the layout of digital platforms with much more not easy requirements'.
Giovanni De Micheli, Professor, Stanford University

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For each arc e E En, a capacity function K: En -+ N, is defined that assigns to each arc a non-negative number. The capacity of all the arcs is one, except for the return arc from t to s which has capacity k, where k is user-specified flow value. K(u,v) 1, K(t, s) k V(u,v)EEn \ {(t,s)} For each arc e E En, a flow function f: En -+ N is defined which assigns to each arc a non-negative number. The flow f(e) on each arc e E En must obey the following: 0 ::; f (e) ::; K (e) and the flow on each vertex v E Vn must satisfy the flow conservation rule.

53 DFG in Fig. 66 Avg. 3. Experimental results for various benchmarks. Note, t: Corresponds to the case of using minimum number of registers for the DFG. 5 Chapter Summary This chapter presented a method to calculate the switching activity external to a set of registers based on the assumption that the joint pdf (probability density function) of the primary input random variables is known or can be calculated. For a scheduled data How graph without cycles, the compatibility 40 POWER OPTIMIZATION AND SYNTHESIS (jJW) (jJW) (jJW) pJk 34720 39680 44640 P;"g 7t 8 9 plata 41676 34502 28308 76396 74182 72948 AR Filter 22t 23 24 173090 165779 159161 109120 114080 119040 282210 279859 278201 EW Filter 31t 32 33 153760 158720 163680 369324 366761 364677 2nd ATF 9t 10 11 215564 208041 200997 29159 22859 16985 44640 49600 54560 73799 72459 71545 Robot Ctrl 29t 30 31 185289 174018 165194 Diff Eq 9t 10 11 329129 322818 318954 79460 78067 76861 FDCT 51t 52 53 34820 28467 22301 187947 182525 177156 143840 148800 153760 44640 49600 54560 252960 257920 262880 440907 440445 440036 Benchmark # of Reg used DFG in Fig.

There is an arc from v to v' for each v E V. IT there is an arc (u, v) E A in the graph Go = G (V, A), there is an arc (u', v) in the new network No. There is also an arc from the source vertex s to every vertex v E V and from every duplicated vertex v' to the sink vertex t. 4. From data flow graph to network (f(v), t) I v K'((t, s)) Nb. E Vo} k,K'((u,v)) = 1 for all u =j:. t, and v =j:. s. The transformations from the data How graph to the final network shown in Fig. 4. 5 A flow f: E~ -+ 31 N, with I f I = k, in the network Nb corresponds to a set of vertex disjoint cliques Xl, X2, ...

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