This paper describes the design of two A/D converters, the Charge Sharing SAR and the Charge Injection SAR, in 22 nm FD SOI technology, for Analog-in-Memory computing in machine learning (ML) applications. The former architecture matches well SRAM-based Matrix-Vector Multipliers (MVM)s, whereas the latter is suitable for integration with SOT-MRAM-based arrays. Both ADCs show remarkable energy figures and area metrics, making the topologies suitable for integration at the periphery of MVM arrays.
Charge Sharing and Charge Injection A/D Converters for Analog In-Memory Computing / Caselli, M.; Papistas, I. A.; Cosemans, S.; Mallik, A.; Debacker, P.; Verkest, D.. - ELETTRONICO. - (2021), pp. 1-4. (Intervento presentato al convegno 19th IEEE International New Circuits and Systems Conference, NEWCAS 2021 tenutosi a Toulone, France nel 2021) [10.1109/NEWCAS50681.2021.9462775].
Charge Sharing and Charge Injection A/D Converters for Analog In-Memory Computing
Caselli M.
;
2021-01-01
Abstract
This paper describes the design of two A/D converters, the Charge Sharing SAR and the Charge Injection SAR, in 22 nm FD SOI technology, for Analog-in-Memory computing in machine learning (ML) applications. The former architecture matches well SRAM-based Matrix-Vector Multipliers (MVM)s, whereas the latter is suitable for integration with SOT-MRAM-based arrays. Both ADCs show remarkable energy figures and area metrics, making the topologies suitable for integration at the periphery of MVM arrays.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
