Every second, our smart phones deliver a wealth of information that can be used to monitor the traffic, the financial transactions, and even the spread of a dangerous disease. The processing of these big data into a meaningful information requires specific machine learning (ML) algorithms, which essentially consist of regression techniques for inference, classification and prediction. The conventional digital computers are not designed to optimally solve these problems with efficient time and energy consumption, which is one of the reasons why the power consumption by data centers worldwide is expected to triple in the next decade. Such a poor energy efficiency is essentially due to the physical separation between the central processing unit (CPU), where data are computed, and the memory, where data are stored, according to classical von Neumann computer architecture. In the frame of our ERC-CoG RESCUE, my group has developed a new paradigm to efficiently execute ML tasks in just one step within the memory. Instead of moving data from the memory to the digital CPU, an analogue computation is directly operated within the data, thus breaking all previous limits of time and energy consumption (10.000x reduction in the number of operations, hence time, and 1.000x in energy). Our in-memory technology is modular and universal, thus can be implemented in any existing memory and computing technology to accelerate ML tasks in future smartphones and data centers. In the ERC-PoC CIRCUS, we aim at bringing this technology to a higher maturity level, demonstrating its scalability and technical feasibility by simulations and realization of a small-scale prototype. In the meantime, we will also perform a comprehensive market search to recognize opportunities and draft an investor-ready business plan for raising future investments to further advance the solution toward industrial exploitation.
Fields of science
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