Architecture and Compilation for Reconfigurable Processors

1. Architecture and Compilation for Reconfigurable Processors Jason Cong, Yiping Fan, Guoling Han, Zhiru Zhang Computer Science Department UCLA Nov 22, 2004

2. Outline • Motivation • Application-specific instruction set compilation • Register file data bandwidth problem • Architecture extension – shadow registers • Shadow register binding • Conclusions

3. Reconfigurable Processor Core Bus CPU Reconfigurable Processor Platform • Reconfigurable processor (RP) core + programmable fabric • RP core supports: Basic instruction set + customized instructions • Programmable fabric implements the customized instructions • Either runtime reconfigurable or pre-synthesized • Example: Nios / Nios II from Altera • Stratix version supported by Nios 3.0 system • 5 extended instruction formats • Up to 2048 instructions for each format

4. a b c 2 5 * * * + + + Motivational Example t1 = a * b; t2 = b * 2;; t3 = c * 5; t4 = t1 + t2; t5 = t2 + t3; t6 = t5 + t4; • t1 = extop1(a, b, 2); • t2 = extop2(b, c, 2, 5); • t3 = t1 + t2; extop2 extop1 *: 2 clock cycles +: 1 clock cycle Extended Instruction Set: Iextop1 expop2 Execution time: 9 clock cycles Execution time: 5 clock cycles Speedup: 1.8

5. Problem Statement Given: • Application program in CDFG G(V, E) • A processor with basic instruction set I • Pattern constraints: • Number of inputs less than Nin; • 1 output; • Total area no more than A Objective: • Generate a pattern library P • Map G to the extended instruction set IP, so that the total execution time is minimized.

6. C ASIP constraints Compilation Pattern Generation / CDFG Pattern Selection Pattern Library Application Mapping Mapped CDFG Instruction Implementation / ASIP Synthesis Simulation Implementation Proposed ASIP Compilation Flow • Extended Instruction Candidates Generation • Satisfying I/O constraints • Extended Instruction Selection • Select a subset to maximize the potential speedup while satisfying the resource constraint • Code Generation • Graph covering • Minimize the total execution time

7. n1 n3 n2 n4 n5 n6 a b c 2 5 * * * + + + Step 1. Pattern Enumeration • Each pattern is a Nin-feasible cone • Cut enumeration is used to enumerate all the Nin-feasible cones [cong et al, FPGA’99] • Basic idea: In topological order, merge the cuts of fan-ins and discards those cuts not Nin-feasible 3-feasible cones: n1: {a, b} n2: {b, 2} n3: {c, 5} n4: {n1, n2}, {n1, b, 2}, {n2, a, b}, {a, b, 2}

8. a b c 2 5 * * * + + + Step 2. Pattern Selection • Basic idea: simultaneously consider speed up, occurrence frequency and area. • Speedup Tsw(p) = total execution time with basic instructions Thw(p)= length of the critical path of scheduled p Speedup(p) = Tsw(p) / Thw(p) • Occurrence • Some pattern instances may be isomorphic • Graph isomorphism test [ Nauty Package ] • Small subgraphs, isomorphism test is very fast Gain(p) = Speedup(p)  Occurrence(p) • Selection under area constraint can be formulated as a 0-1 knapsack problem n3 n2 n1 n4 n5 n6 Pattern *+ Tsw= 3 Thw= 2 Speedup = 1.5

9. Step 3. Application Mapping • Assume execution on an in-order, single-issue processor • Cover each node in G(V, E) with the extended instruction set to minimize the execution time. • Trivial pattern – software execution time • Nontrivial pattern – hardware execution time • Total execution time = Sum of execution time of instance patterns after application mapping • Theorem: The application mapping problem is equivalent to the library-based minimum-area technology mapping problem.

10. Speedup and Resource Overhead on NIOS

11. Simulation Environment • Simplescalar v3.0 • Benchmarks • From Mediabench suite • Machine Configuration • Single issue in-order processor (ARM like) • DL1: 8KB, 4-way, 1 cycle • IL1: 8KB, direct mapped, 1 cycle • Unified L2: 256KB, 4-way, 8 cycle • Functional units: 2 IntAdd, 1 IntMult, 1 FPAdd, 1 FPMult • Reconfigurable units • critical path latency of the collapsed instructions

12. Pattern Distribution Most of the patterns have less than 7 nodes inside

13. Ideal Speedup under Different Input Size Constraints

14. Outline • Motivation • Application-specific instruction set compilation • Register file data bandwidth problem • Architecture extension – shadow registers • Shadow register binding • Conclusions

15. Register File Bandwidth Problem • Most of the speedup comes from clusters with more than two inputs • 2-port register file in embedded processors • Need extra cycles to transfer data for extended instructions with more than 2 inputs • Speedup drop due to communication overhead

16. Speedup Drop with Different Input Constraints • Move operation takes one cycle • 46% speedup drop on average

17. Outline • Motivation • Application-specific instruction set compilation • Register file data bandwidth problem • Architecture extension – shadow registers • Shadow register binding • Conclusions

18. Architecture Extensions • Existing Solutions • Dedicated Data Link • Avoid potential resource contention through bus • Need extra cycles for communication • Employed in Microblaze from Xilinx • Multiport Register File • Low utilization when executing basic instructions • Area and power grows cubically • Register File Replication • Predetermined one-to-one correspondence • Resource waste in terms of area and power • Limit compiler optimization

19. Our Approach – Shadow Registers • Core registers are augmented by an extra set of shadow registers • Conditionally written • Used only by the custom logic

20. Shadow Registers • Controlling the shadow register • Advantages and limitations • Cost-efficient for small number of shadow registers • Only need a few control signals to be added • Opportunity for compiler optimization • Require extra control bits

21. Outline • Motivation • Application-specific instruction set compilation • Register file data bandwidth problem • Architecture extension – shadow registers • Shadow register binding • Conclusions

22. Internal Representation 2-level CDFG representation • 1st level: control flow graph • 2nd level: data flow graph • Computation node latency & scheduled time slot • Data edge lifetime • Variable lifetime 1 i1 = …; i2 = ext1 (…, i1, …); i3 = …; i4 = ext2 (…, i1, …); i5 = ext3 (…, i3, …); i6 = ext4 (…, i3, …); e1 2 e2 3 e3 4 e4 5 6 Life time e1 = [2, 2] Life time e2 = [2, 4] Life time i1 = [2, 4]

23. Observation • 2-port register file • 3-input extended instruction • Without shadow register 4 additional moves • Binding for 1 register 1 i1 = …; i2 = ext1 (…, i1, …); i3 = …; i4 = ext2 (…, i1, …); i5 = ext3 (…, i3, …); i6 = ext4 (…, i3, …); e1 2 e2 3 e3 4 e4 5 Binding 1: either i1 or i3 in shadow register save 2 moves 6 Binding 2: save 3 moves

24. Register Binding • Which operands should be bound? • Each input could be a candidate • Binding different candidates leads to different savings • Unaffordable to try all the combinations

25. One Shadow Register Binding Problem • Problem formulation: • Given A scheduled DFG and one shadow register • Objective Bind variables to shadow register Minimize the number of moves

26. Algorithm for Binding One Shadow Register • Weighted compatibility graph • Vertex <-> data edge in the DFG • Weight <-> # saves if the value is kept in the register • Edge <-> lifetimes don’t overlap • Theorem: • Binding problem is equivalent to find a maximum weighted chain in the compatibility graph • Can be optimally solved in time O(|V’| + |E’|) • Extension to K-shadow registers

27. Experimental Results (1) Speedup under different number of shadow registers for 3-input extended instructions

28. Experimental Results (2) Speedup under different number of shadow registers for 4-input extended instructions

29. Conclusions • Proposed and developed complete compilation flow • Observed and quantitatively analyzed data bandwidth problem • Proposed novel architecture extension and efficient register binding algorithm

30. Thank You