Approximate Memory with Protected Static Allocation

Autor(es)

Fabricio Filho, Joao ; Felzmann, Isaias ; Wanner, Lucas ; IEEE Comp Soc

• Tipo de documento: Artigo Científico
• Fonte: 2022 IEEE 34TH INTERNATIONAL SYMPOSIUM ON COMPUTER ARCHITECTURE AND HIGH PERFORMANCE COMPUTING (SBAC-PAD 2022); v. N/A, p. 9-pg., 2022-01-01.

Resumo

Approximate memories provide energy savings or performance improvements at the cost of occasional errors in stored data. Applications that tolerate errors on their data profit from this trade-off by controlling these errors to not affect critical data. This control usually involves programmer intervention with annotations in the source code. To avoid annotations, some techniques protect critical data that are common on many applications, isolating specific memory regions from errors. In this work, we propose and explore alternatives for the protection of application critical data by managing a supervisor execution environment with an approximate memory system. We expose only dynamically allocated data to errors with secure data manipulation through an approximate allocation scheme that divide stored data based on the approximation of the heap area. We evaluate 6 applications with different data access profiles and obtain up to 20% of energy savings. (AU)

• Processo FAPESP: 18/24177-0 - Suporte Arquitetural para Computação Aproximada
• Beneficiário: Isaías Bittencourt Felzmann
• Modalidade de apoio: Bolsas no Brasil - Doutorado