GROK-LAB: Generating real on-chip knowledge for intra-cluster delays using timing extraction
B Gojman, S Nalmela, N Mehta, N Howarth… - ACM Transactions on …, 2014 - dl.acm.org
ACM Transactions on Reconfigurable Technology and Systems (TRETS), 2014•dl.acm.org
Timing Extraction identifies the delay of fine-grained components within an FPGA. From
these computed delays, the delay of any path can be calculated. Moreover, a comparison of
the fine-grained delays allows a detailed understanding of the amount and type of process
variation that exists in the FPGA. To obtain these delays, Timing Extraction measures, using
only resources already available in the FPGA, the delay of a small subset of the total paths in
the FPGA. We apply Timing Extraction to the Logic Array Block (LAB) on an Altera Cyclone III …
these computed delays, the delay of any path can be calculated. Moreover, a comparison of
the fine-grained delays allows a detailed understanding of the amount and type of process
variation that exists in the FPGA. To obtain these delays, Timing Extraction measures, using
only resources already available in the FPGA, the delay of a small subset of the total paths in
the FPGA. We apply Timing Extraction to the Logic Array Block (LAB) on an Altera Cyclone III …
Timing Extraction identifies the delay of fine-grained components within an FPGA. From these computed delays, the delay of any path can be calculated. Moreover, a comparison of the fine-grained delays allows a detailed understanding of the amount and type of process variation that exists in the FPGA. To obtain these delays, Timing Extraction measures, using only resources already available in the FPGA, the delay of a small subset of the total paths in the FPGA. We apply Timing Extraction to the Logic Array Block (LAB) on an Altera Cyclone III FPGA to obtain a view of the delay down to near-individual LUT SRAM cell granularity, characterizing components with delays on the order of tens to a few hundred picoseconds with a resolution of ±3.2ps, matching the expected error bounds. This information reveals that the 65nm process used has, on average, random variation of σ μ =4.0% with components having an average maximum spread of 83ps. Timing Extraction also shows that as VDD decreases from 1.2V to 0.9V in a Cyclone IV 60nm FPGA, paths slow down, and variation increases from σ μ =4.3% to σ μ =5.8%, a clear indication that lowering VDD magnifies the impact of random variation.
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