SALM aCAM reduces read energy by 33% versus 6T2M at same latency, eliminates gain and crosstalk limits, and maintains near-software accuracy in decision-tree workloads via latch sharing and dataset-aware optimization in 22 nm FD-SOI.
The use of multiple measurements in taxonomic problems
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Bloom filter encodings convert data samples to bit arrays that support comparable classifier performance to raw data across text, time-series, tabular, and image datasets while delivering consistent memory savings.
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A Fast and Energy-Efficient Latch-Based Memristive Analog Content-Addressable Memory
SALM aCAM reduces read energy by 33% versus 6T2M at same latency, eliminates gain and crosstalk limits, and maintains near-software accuracy in decision-tree workloads via latch sharing and dataset-aware optimization in 22 nm FD-SOI.
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Bloom Filter Encoding for Machine Learning
Bloom filter encodings convert data samples to bit arrays that support comparable classifier performance to raw data across text, time-series, tabular, and image datasets while delivering consistent memory savings.