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Montek Singh COMP790-084 Oct 11, 2011
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Today’s topics: ◦ more on error metrics ◦ more applications ◦ architectures and design tools ◦ challenges and benefits ◦ open questions
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For arithmetic units, error metric based upon an error threshold, δ ◦ errors < δ are tolerable ◦ p δ = prob (err < δ)
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Applications which harness probabilistic behavior ◦ algorithms with repeated execution with the same inputs resulting in distinct outcomes (with some prob. distribution) Separate algorithm into deterministic and probabilistic parts
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Bayesian Inference ◦ statistical inference technique mimicking human decision-making process ◦ set of hypotheses and probability weights ◦ each observation leads to a revision of prob weights ◦ Example:
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Example ◦ Given prob of rain prob of sprinkler being on given rain ◦ Find: prob of rain given that the grass is wet Implemented using PCMOS
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Random Neural Networks ◦ Poisson process models the “firing” of a neuron Probabilistic Cellular Automata ◦ Each cell’s next state is a function of its neighbors ◦ Next state could be 0 or 1 with certain prob Hyper Encryption ◦ Random seed generated using PCMOS
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Applications that tolerate probabilistic behavior ◦ multimedia mostly ◦ signal processing ◦ others?
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Different partitioning of deterministic vs. probabilistic parts of an algorithm
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Host (deterministic) vs. coprocessor (probabilistic) partitioning
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Comparison ◦ Host = deterministic ◦ without coprocessor: software only ◦ with coprocessor: using PCMOS using CMOS
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Quality of randomness using PCMOS vs. pseudo RNG
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Benefits ◦ …? Challenges ◦ …?
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Architectural questions ◦ Which design is better in terms of E-p tradeoff? ◦ Example: Which adder is better: carry-skip or ripple-carry? carry-skip adder has faster propagation time ripple-carry adder consumes less energy But: carry-skip adder may be better when there are delay-induced errors! Design tools ◦ What type of tool support is needed? ◦ Simulation and validation?
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