PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.
With the explosive growth of artificial intelligence (AI) has come an immense and growing computational burden that is outpacing the rate of traditional logic scaling. To tackle this challenge, IBM is pioneering an analog in-memory compute (IMC) technology that promises to considerably reduce the energy consumption needed for AI workloads by performing the computation directly in memory using resistive non-volatile memory (NVM) devices. This talk will detail the materials and device innovations that enable analog IMC and the challenges encountered in creating a scalable technology. In particular, the importance of controlling variability for a resistive processing unit will be highlighted. In addition, the novel metrology techniques needed to optimize the performance of the key analog materials will be discussed. It will be shown that by comprehending the materials and stochastic characteristics of the NVM devices and co-optimizing with algorithms and architectures, large improvements in energy efficiency can be obtained.
Vijay Narayanan
"Analog in-memory computing with embedded resistive devices: metrology challenges, and opportunities", Proc. SPIE 12955, Metrology, Inspection, and Process Control XXXVIII, 1295503 (10 April 2024); https://doi.org/10.1117/12.3014734
ACCESS THE FULL ARTICLE
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.
The alert did not successfully save. Please try again later.
Vijay Narayanan, "Analog in-memory computing with embedded resistive devices: metrology challenges, and opportunities," Proc. SPIE 12955, Metrology, Inspection, and Process Control XXXVIII, 1295503 (10 April 2024); https://doi.org/10.1117/12.3014734