ReRAM test chip demonstrations for AI at the Edge showing simultaneous object classification in one iteration for facial recognition and car license plate recognition featured at Embedded Vision Summit
SANTA CLARA, Calif.-- May 14, 2018 --Crossbar, the ReRAM technology leader, today announced its ReRAM technology for artificial intelligence (AI) is available for licensing. The company will demonstrate its test chip for AI at the Edge at the Embedded Vision Summit booth number #606, at the San Jose Convention Center. The demos will show Crossbar’s ReRAM for AI in action on facial recognition for accelerating object classification with deterministic performance.
In addition, Sylvain Dubois, vice president of business development and strategic marketing, will speak on the topic of a “New Memory-Centric Architecture Needed for AI” on Wed., May 23 at 12 p.m. to 12:30 p.m. at the Summit.
“The biggest challenge facing engineers for AI today is overcoming the memory speed and power bottleneck in the current architecture to get faster data access while lowering the energy cost,” said Dubois. “By enabling a new, memory-centric non-volatile architecture like ReRAM, the entire trained model or knowledge base can be on-chip, connected directly to the neural network with the potential to achieve massive energy savings and performance improvements, resulting in a greatly improved battery life and a better user experience.”
About Crossbar, Inc.
Crossbar is the leader in ReRAM technology, enabling kilobytes to terabytes of always-on data storage to be embedded into any processor, microcontroller, FPGA or as a standalone memory chip. Crossbar ReRAM lets designers rethink the compute/storage paradigm, free from the constraints of traditional flash and DRAM memories. From “persistent memory” that brings data closer to CPU to “cognitive memory” that enables in-memory computing without a host CPU, ReRAM is ushering in a new era of data storage and processing for both edge and cloud computing. For more information, visit crossbar-inc.com .