DARPA Hive processor could boost computing efficiency by 1000 times
HIVE is not von Neumann because of the sparseness of its data and its ability to simultaneously perform different processes on different areas of memory simultaneously," Trung said. "This non-von-Neumann approach allows one big map that can be accessed by many processors at the same time, each using its own local scratch-pad memory while simultaneously performing scatter-and-gather operations across global memory.
DARPA's new arithmetic-processing-unit (APU) optimized for graph analytics plus the new memory architecture chips are specified to use 1,000-times less power than using today's supercomputers. The participants, especially Intel and Qualcomm, will also have the rights to commercialize the processor and memory architectures they invent to create a HIVE.
The graph analytics processor is needed, according to DARPA, for Big Data problems, which typically involve many-to-many rather than many-to-one or one-to-one relationships for which today's processors are optimized. A military example, according to DARPA, might be the the first digital missives of a cyberattack. A civilian example, according to Intel, might be all the people buying from Amazon mapped to all the items each of them bought (clearly delineating the many-to-many relationships as people-to-products).
HIVE is developing a new category of server processors specifically designed to handle the data workloads of today and tomorrow.
The quintet of performers includes a mix of large commercial electronics firms, a national laboratory, a university, and a veteran defense-industry company: Intel Corporation (Santa Clara, California), Qualcomm Intelligent Solutions (San Diego, California), Pacific Northwest National Laboratory (Richland, Washington), Georgia Tech (Atlanta, Georgia), and Northrop Grumman (Falls Church, Virginia).
Central to HIVE is the creation of a "graph analytics processor," which incorporates the power of graphical representations of relationships in a network more efficiently than traditional data formats and processing techniques. Examples of these relationships among data elements and categories include person-to-person interactions as well as seemingly disparate links between, say, geography and changes in doctor visit trends or social media and regional strife. In combination with emerging machine learning and other artificial intelligence techniques that can categorize raw data elements, and by updating the elements in the graph as new data becomes available, a powerful graph analytics processor could discern otherwise hidden causal relationships and stories among the data elements in the graph representations.
If HIVE is successful, it could deliver a graph analytics processor that achieves a thousandfold improvement in processing efficiency over today's best processors, enabling the real-time identification of strategically important relationships as they unfold in the field rather than relying on after-the-fact analyses in data centers. "This should empower data scientists to make associations previously thought impractical due to the amount of processing required," said Tran. These could include the ability to spot, for example, early signs of an Ebola outbreak, the first digital missives of a cyberattack, or even the plans to carry out such an attack before it happens.
There is $75 million in funding for DARPA in support of a new, public-private "electronics resurgence" initiative. The initiative seeks to undergird a new era of electronics in which advances in performance will be catalyzed not just by continued component miniaturization but also by radically new microsystem materials, designs, and architectures. The new funds will supplement the Agency's FY 2018 R&D portfolio in electronics, photonics, and related systems to create a coordinated effort valued at more than $200 million, to be further supplemented by significant commercial sector investments.
