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Hyperdimensional computing (HDC) is an emerging computing approach inspired by patterns of neural activity in the human brain. This unique type of computing can allow artificial intelligence systems to retain memories and process new information based on data or scenarios it previously encountered.

Most HDC systems developed in the past only perform well on specific tasks, such as natural language processing (NLP) or time series problems. In a paper published in Nature Electronics, researchers at IBM Research- Zurich and ETH Zurich presented a new HDC system that performs all core computations in-memory and that could be applied to a variety of tasks.

"Our work was initiated by the natural fit between the two concepts of in-memory computing and hyperdimensional computing," Abu Sebastian and Abbas Rahimi, the two lead researchers behind the study, told TechXplore. "At IBM Research- Zurich, we have been developing in-memory computing platforms based on phase-change memory (PCM), while at ETH Zurich, we have been exploring a brain-inspired computing paradigm called hyperdimensional computing."

In their past work, the researchers observed that the primary operations involved in HDC, namely encoding and associative memory search, both involve the manipulation and comparison of large distributed patterns within the system's memory. As a result of this characteristic, these systems can be efficiently fabricated using PCM crossbar arrays, in a way that enables the advantages of analog in-memory computing. "This tailor-made combination not only avoids the von Neumann bottleneck (aka memory wall), but also significantly improves energy efficiency as well as robustness against variability, noise, and failures," Sebastian and Rahimi explained. "Almost two years ago, this observation prompted us to initiate a joint research in this direction between ETH and IBM."