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Temporal Logic Replication for Dynamically Reconfigurable FPGA Partitioning Wai-Kei Mak Dept. of Computer Science and Engineering University of South Florida Evangeline F.Y. Young Dept. of Computer Science and Engineering The Chinese University of Hong Kong
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Outline I.Dynamically reconfigurable FPGA II.Temporal partitioning = Conventional partitioning? III.Temporal logic replication What? Why? How? IV.Experimental results V.Conclusions
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Dynamically Reconfigurable FPGA zStore multiple contexts on chip. zReuse logic blocks and wire segments dynamically. zThe contexts stored can correspond to the multiple stages of a large circuit.
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Temporal Circuit Partitioning zTemporal partitioning multiple stages execute sequentially z Spatial partitioning multiple components execute concurrently
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Temporal Logic Replication zCan reduce buffering requirement. zEffectively utilize available slack logic capacity.
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Temporal Constraints zFor a net n = (v 1, {v 2, …, v p }), require s(v 1 ) s(v j ), j=2,…,p, if v 1 is a combinational node
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Temporal Constraints (Cont’d) require s(v j ) s(v 1 ), j=2,…,p, if v 1 is a flip-flop node
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Temporal Partitioning with Replication Problem: Partition given circuit into pre-defined # stages satisfying all temporal constraints. Objective: Minimize buffers required between stages. Proposal: Utilize available slack logic capacity to reduce signal buffering. Solution: An effective 2-step approach.
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2-Step Approach Step 1: Compute a temporal partition w/o replication. Step 2: Repeatedly identify the bottleneck stage and apply replication for that stage.
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Advantages of 2-Step Approach zWill not replicate unnecessarily. zAll temporal constraints are already satisfied when replicating.
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Min-Area Min-Cut Replication Let stage i be the bottleneck stage. Min-Cut Replication Compute a subset of nodes R i in stage i for replication into stage i+1 to maximally reduce the communication cost at stage i. Min-Area Min-Cut Replication Compute a minimum subset of nodes R i in stage i for replication into stage i+1 to maximally reduce the communication cost at stage i.
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Optimal Solution for Min-Area Min-Cut Replication Observation 1 : The min-cut replication problem can be solved by computing a minimum cut (V i -R i,R i ) in stage i. Observation 2 : The min-area min-cut replication problem can be solved by computing a minimum cut (V i -R i,R i ) in stage i s.t. |R i | is minimized. Let V i = set of nodes in stage i.
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Example A pre-partition: Computing a minimum cut in stage 2:
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Example (Cont’d) Computed R 2 = {j}
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Network Modeling zNeed to ensure that cut size = buffer requirement zFor a net (v 1, {v 2, …, v p }),
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The Case of Limited Slack Logic Capacity zThe solution of min-area min-cut replication suffices if slack logic capacity is sufficiently large. Otherwise, |R i | exceeds the slack, then use a heuristic to reduce R i. Use a repeated max-flow min-cut heuristic to gradually reduce R i (so cut size is only increased gradually). zH. Yang, D.F. Wong, “Efficient Network Flow based Min-Cut Balanced Partitioning”, ICCAD’94.
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Algorithm Input: Stage area bound A. 1. Network modeling for bottleneck stage i. 2. Compute min-cut (V i -R i,R i ) s.t. |R i | is minimized. 3. If |V i+1 |+|R i | A, stop and return R i. 4. Collapse a node in R i with all nodes in V i -R i, goto 2.
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Experimental Results Circuit#buf w/o rep.#buf w/ rep.Imprv. %Rep. % C35401981942.020.48 C53151401297.860.67 C6288836324.104.41 C755221017616.193.12 S132076886692.762.54 S158507616998.153.59 S35932272926363.412.48 S38417219421044.100.63 S38584228021376.270.98
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Conclusions zProposed temporal logic replication to reduce buffering requirement in DRFPGA partitioning. zPresented an effective 2-step approach. zFormulated and optimally solved the min-area min-cut replication problem. zExtended to case of limited slack logic capacity. zIn the paper, a new timing-driven temporal partitioning algorithm was introduced to compute pre-partition.
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