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Reconfigurable Architectures. Forces that drive a Reconfigurable Architecture Price Mass production 100K to millions Experimental 1 to 10’s Granularity of reconfiguration Fine grain Course Grain Degree of system integration/coupling Tightly Loosely.
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Reconfigurable Architectures • Forces that drive a Reconfigurable Architecture • Price • Mass production 100K to millions • Experimental 1 to 10’s • Granularity of reconfiguration • Fine grain • Course Grain • Degree of system integration/coupling • Tightly • Loosely All are a function of the application that will run on the Architecture
Example Points in (Price,Granularity,Coupling) Space $1M’s Exec Int Intel / AMD Decode Store float RFU Processor Price Coupling Tight $100’s Loose Coarse PC Ethernet Granularity ML507 Fine
What’s the point of a Reconfigurable Architecture • Performance metrics • Computational • Throughput • Latency • Power • Total power dissipation • Thermal • Reliability • Recovery from faults Increase application performance!
Typical Approach for Increasing Performance • Application/algorithm implemented in software • Often easier to write an application in software • Profile application (e.g. gprof) • Determine where the application is spending its time • Identify kernels of interest • e.g. application spends 90% of its time in function matrix_multiply() • Design custom hardware/instruction to accelerate kernel(s) • Analysis to kernel to determine how to extract fine/coarse grain parallelism (does any parallelism even exist?) Amdahl’s Law!
Granularity: Coarse Grain • rDPA: reconfigurable Data Path Array • Function Units with programmable interconnects Example ALU ALU ALU ALU ALU ALU ALU ALU ALU
Granularity: Coarse Grain • rDPA: reconfigurable Data Path Array • Function Units with programmable interconnects Example ALU ALU ALU ALU ALU ALU ALU ALU ALU
Granularity: Coarse Grain • rDPA: reconfigurable Data Path Array • Function Units with programmable interconnects Example ALU ALU ALU ALU ALU ALU ALU ALU ALU
Granularity: Fine Grain CLB CLB CLB CLB CLB CLB CLB CLB Configurable Logic Block CLB CLB CLB CLB CLB CLB CLB CLB • FPGA: Field Programmable Gate Array • Sea of general purpose logic gates
Granularity: Fine Grain Configurable Logic Block • FPGA: Field Programmable Gate Array • Sea of general purpose logic gates CLB CLB CLB CLB CLB CLB CLB CLB CLB CLB CLB CLB CLB CLB CLB
Granularity: Fine Grain Configurable Logic Block • FPGA: Field Programmable Gate Array • Sea of general purpose logic gates CLB CLB CLB CLB CLB CLB CLB CLB
Granularity: Trade-offs Trade-offs associated with LUT size Example: 2-LUT (4=2x2 bits) vs. 10-LUT (1024=32x32 bits) 1024-bits 2-LUT 10-LUT Microprocessor 1024-bits
Granularity: Trade-offs Trade-offs associated with LUT size Example: 2-LUT (4=2x2 bits) vs. 10-LUT (1024=32x32 bits) 1024-bits 2-LUT 4 op 3 A 3 10-LUT Microprocessor 3 B 1024-bits
Granularity: Trade-offs Trade-offs associated with LUT size Example: 2-LUT (4=2x2 bits) vs. 10-LUT (1024=32x32 bits) 1024-bits 2-LUT 4 op 3 A 3 10-LUT Microprocessor 3 B 4 op 1024-bits 3 A 3 B 3 4 op 3 3 A B 3
Granularity: Trade-offs Trade-offs associated with LUT size Example: 2-LUT (4=2x2 bits) vs. 10-LUT (1024=32x32 bits) 1024-bits 2-LUT 4 op A 3 10-LUT Microprocessor 3 3 B 1024-bits op A 3 3 3 B 4 op 3 A 3 B 3
Granularity: Trade-offs Trade-offs associated with LUT size Example: 2-LUT (4=2x2 bits) vs. 10-LUT (1024=32x32 bits) 1024-bits 2-LUT 4 4 op op A A 3 3 10-LUT Microprocessor 3 3 3 3 B B 1024-bits 4 op 3 A 3 3 B
Granularity: Trade-offs Trade-offs associated with LUT size Example: 2-LUT (4=2x2 bits) vs. 10-LUT (1024=32x32 bits) 1024-bits 2-LUT 10-LUT Bit logic and constants 1024-bits
Granularity: Trade-offs Trade-offs associated with LUT size Example: 2-LUT (4=2x2 bits) vs. 10-LUT (1024=32x32 bits) 1024-bits 2-LUT 10-LUT Bit logic and constants 1024-bits (A and “1100”) or (B or “1000”)
Granularity: Trade-offs Trade-offs associated with LUT size Example: 2-LUT (4=2x2 bits) vs. 10-LUT (1024=32x32 bits) 1024-bits 2-LUT A 10-LUT B Bit logic and constants 1024-bits (A and “1100”) or (B or “1000”)
Granularity: Trade-offs Trade-offs associated with LUT size Example: 2-LUT (4=2x2 bits) vs. 10-LUT (1024=32x32 bits) 1024-bits 2-LUT AND 4 A 10-LUT 1 Bit logic and constants 1024-bits OR Area that was required using 2-LUTS (A and “1100”) or (B or “1000”) 0 OR 4 B It’s much worse, each 10-LUT only has one output
Granularity: Example Architectures • Fine grain: GARP • Course grain: PipeRench
Granularity: GARP Memory D-cache I-cache CPU RFU Config cache Garp chip
Granularity: GARP Memory RFU Execution (16, 2-bit) control (1) D-cache I-cache CPU RFU N Config cache PE (Processing Element) Garp chip
Granularity: GARP Memory RFU Execution (16, 2-bit) control (1) D-cache I-cache CPU RFU N Config cache PE (Processing Element) Garp chip Example computations in one cycle A<<10 | (b&c) (A-2*b+c)
Granularity: GARP Memory • Impact of configuration size • 1 GHz bus frequency • 128-bit memory bus • 512Kbits of configuration size D-cache I-cache On a RFU context switch how long to load a new full configuration? CPU RFU 4 microseconds An estimate of amount of time for the CPU perform a context switch is ~5 microseconds Config cache Garp chip ~2x increase context switch latency!!
Granularity: GARP Memory RFU Execution (16, 2-bit) control (1) D-cache I-cache CPU RFU N Config cache PE (Processing Element) Garp chip • “The Garp Architecture and C Compiler” • http://www.cs.cmu.edu/~tcal/IEEE-Computer-Garp.pdf
Granularity: PipeRench • Coarse granularity • Higher (higher) level programming • Reference papers • PipeRench: A Coprocessor for Streaming Multimedia Acceleration (ISCA 1999): http://www.cs.cmu.edu/~mihaib/research/isca99.pdf • PipeRench Implementation of the Instruction Path Coprocessor (Micro 2000): http://class.ee.iastate.edu/cpre583/papers/piperench_Micro_2000.pdf
Granularity: PipeRench PE PE PE PE PE PE PE PE PE 8-bit ALU 8-bit ALU 8-bit ALU 8-bit ALU 8-bit ALU 8-bit ALU 8-bit ALU 8-bit ALU 8-bit ALU Reg file Reg file Reg file Reg file Reg file Reg file Reg file Reg file Reg file Interconnect Global bus Interconnect
Granularity: PipeRench Cycle 1 2 3 4 5 6 Pipeline stage 0 1 2 3 4 PE PE PE PE PE PE PE PE PE PE PE PE
Granularity: PipeRench Cycle 1 2 3 4 5 6 Pipeline stage 0 1 2 3 4 0 PE PE PE PE PE PE PE PE PE PE PE PE
Granularity: PipeRench Cycle 1 2 3 4 5 6 Pipeline stage 0 1 2 3 4 0 0 PE PE PE PE 1 PE PE PE PE PE PE PE PE
Granularity: PipeRench Cycle 1 2 3 4 5 6 Pipeline stage 0 1 2 3 4 0 0 0 PE PE PE PE 1 1 2 PE PE PE PE PE PE PE PE
Granularity: PipeRench Cycle 1 2 3 4 5 6 Pipeline stage 0 1 2 3 4 0 0 0 PE PE PE PE 1 1 1 2 2 PE PE PE PE 3 PE PE PE PE
Granularity: PipeRench Cycle 1 2 3 4 5 6 Pipeline stage 0 1 2 3 4 0 0 0 PE PE PE PE 1 1 1 2 2 2 PE PE PE PE 3 3 4 PE PE PE PE
Granularity: PipeRench Cycle 1 2 3 4 5 6 Pipeline stage 0 1 2 3 4 0 0 0 0 PE PE PE PE 1 1 1 2 2 2 PE PE PE PE 3 3 3 4 4 PE PE PE PE
Granularity: PipeRench Cycle 1 2 3 4 5 6 Pipeline stage 0 1 2 3 4 0 0 0 0 PE PE PE PE 1 1 1 2 2 2 PE PE PE PE 3 3 3 4 4 PE PE PE PE Cycle 1 2 3 4 5 6 Pipeline stage 0 1 2
Granularity: PipeRench Cycle 1 2 3 4 5 6 Pipeline stage 0 1 2 3 4 0 0 0 0 PE PE PE PE 1 1 1 2 2 2 PE PE PE PE 3 3 3 4 4 PE PE PE PE Cycle 1 2 3 4 5 6 Pipeline stage 0 1 2 0
Granularity: PipeRench Cycle 1 2 3 4 5 6 Pipeline stage 0 1 2 3 4 0 0 0 0 PE PE PE PE 1 1 1 2 2 2 PE PE PE PE 3 3 3 4 4 PE PE PE PE Cycle 1 2 3 4 5 6 Pipeline stage 0 1 2 0 0 1
Granularity: PipeRench Cycle 1 2 3 4 5 6 Pipeline stage 0 1 2 3 4 0 0 0 0 PE PE PE PE 1 1 1 2 2 2 PE PE PE PE 3 3 3 4 4 PE PE PE PE Cycle 1 2 3 4 5 6 Pipeline stage 0 1 2 0 0 0 1 1 2
Granularity: PipeRench Cycle 1 2 3 4 5 6 Pipeline stage 0 1 2 3 4 0 0 0 0 PE PE PE PE 1 1 1 2 2 2 PE PE PE PE 3 3 3 4 4 PE PE PE PE Cycle 1 2 3 4 5 6 Pipeline stage 0 1 2 0 0 0 3 1 1 1 2 2
Granularity: PipeRench Cycle 1 2 3 4 5 6 Pipeline stage 0 1 2 3 4 0 0 0 0 PE PE PE PE 1 1 1 2 2 2 PE PE PE PE 3 3 3 4 4 PE PE PE PE Cycle 1 2 3 4 5 6 Pipeline stage 0 1 2 0 0 0 3 3 1 1 1 4 2 2 2
Granularity: PipeRench Cycle 1 2 3 4 5 6 Pipeline stage 0 1 2 3 4 0 0 0 0 PE PE PE PE 1 1 1 2 2 2 PE PE PE PE 3 3 3 4 4 PE PE PE PE Cycle 1 2 3 4 5 6 Pipeline stage 0 1 2 0 0 0 3 3 3 1 1 1 4 4 2 2 2 0
Degree of Integration/Coupling • Independent Reconfigurable Coprocessor • Reconfigurable Fabric does not have direct communication with the CPU • Processor + Reconfigurable Processing Fabric • Loosely coupled on the same chip • Tightly coupled on the same chip
Degree of Integration/Coupling CPU Execute Write Back Memory Decode ALU Fetch FPU DMA Controller L1 Cache Main Memory Memory Controller L2 Cache I/O Controller USB PCI PCI-Express SATA Hard Drive NIC
Degree of Integration/Coupling CPU Execute Write Back Memory Decode ALU Fetch FPU DMA Controller L1 Cache Main Memory Memory Controller L2 Cache I/O Controller USB PCI PCI-Express SATA RPF Hard Drive NIC
Degree of Integration/Coupling CPU Execute Write Back RPF Memory Decode ALU Fetch FPU DMA Controller L1 Cache Main Memory Memory Controller L2 Cache I/O Controller USB PCI PCI-Express SATA Hard Drive NIC
Degree of Integration/Coupling CPU Execute Write Back Memory Decode ALU Fetch FPU DMA Controller L1 Cache Config I/F Main Memory Memory Controller L2 Cache I/O Controller RPF USB PCI PCI-Express SATA Hard Drive NIC
Degree of Integration/Coupling CPU Execute Write Back Memory Decode ALU Fetch FPU DMA Controller L1 Cache Config I/F Main Memory Memory Controller L2 Cache I/O Controller RPF USB PCI PCI-Express SATA Hard Drive NIC
Degree of Integration/Coupling CPU Execute Write Back Memory Decode ALU Fetch FPU DMA Controller L1 Cache Config I/F Main Memory Memory Controller L2 Cache I/O I/O Controller RPF USB PCI PCI-Express SATA Hard Drive NIC
Degree of Integration/Coupling CPU Execute Write Back Memory Decode ALU Fetch FPU RFU DMA Controller L1 Cache Main Memory Memory Controller L2 Cache I/O Controller USB PCI PCI-Express SATA Hard Drive NIC