In-Place Computing: High-Performance Search
This presentation details an in-place associative computing technology that changes the concept of computing from serial data processing—where data is moved back and forth between the processor and memory—to massive parallel data processing, compute, and search in-place directly in the main processing array. This in-place associative computing technology removes the bottleneck at the IO between the processor and memory, resulting in significant performance-over-power ratio improvement compared to conventional methods that use CPU and GPGPU (General Purpose GPU) along with DRAM. Target applications include, convolutional neural networks, recommender systems for e-commerce, and data mining tasks such as prediction, classification, and clustering.
Avidan Akerib is VP of the Associative Computing business unit at GSI Technology. He holds a PhD from the Weizmann Institute of Science where he developed the theory of associative computing and applications for image processing and graphics. Avidan has over 30 years of experience in parallel computing, image processing and pattern recognition, and associative processing. He holds over 20 patents related to parallel computing and associative processing.