TY - JOUR
T1 - Evaluating the performance and energy of STT-RAM caches for real-world wearable workloads
AU - Gajaria, Dhruv
AU - Adegbija, Tosiron
N1 - Funding Information:
This work was supported in part by the National Science Foundation, USA under grant CNS-1844952 . Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.
Publisher Copyright:
© 2022 Elsevier B.V.
PY - 2022/11
Y1 - 2022/11
N2 - Wearable devices have grown exponentially in popularity in both consumer and industrial applications in recent years. Despite their stringent area and energy constraints, these devices are processing increasingly complex and data-rich workloads, necessitating innovative area- and energy-efficient computing solutions and architectures. This paper explores spin-transfer torque RAM (STT-RAM) as a candidate for designing area- and energy-efficient wearable processor caches. First, we analyze the memory footprints of 16 real-world wearable workloads and compare them to general-purpose benchmarks like SPEC 2017 and MiBench. Then, we analyze the wearable workloads’ memory characteristics to reveal that wearable workloads are highly read-intensive, making them less vulnerable than general-purpose workloads to the write latency/energy overheads inherent in STT-RAM caches. Our analysis also reveals that wearable workloads have low cache variability needs, and their cache blocks exhibit short and stable lifetimes. Against the background of this analysis, we explore the tradeoffs of STT-RAM cache architecture designs for the wearable workloads. Specifically, we explore a simple adaptable design that aims to optimize latency or energy, based on runtime needs, without introducing significant design or area overhead. Our analysis shows that STT-RAM caches offer much promise for energy- and area-efficient wearable computing, without introducing much performance overheads.
AB - Wearable devices have grown exponentially in popularity in both consumer and industrial applications in recent years. Despite their stringent area and energy constraints, these devices are processing increasingly complex and data-rich workloads, necessitating innovative area- and energy-efficient computing solutions and architectures. This paper explores spin-transfer torque RAM (STT-RAM) as a candidate for designing area- and energy-efficient wearable processor caches. First, we analyze the memory footprints of 16 real-world wearable workloads and compare them to general-purpose benchmarks like SPEC 2017 and MiBench. Then, we analyze the wearable workloads’ memory characteristics to reveal that wearable workloads are highly read-intensive, making them less vulnerable than general-purpose workloads to the write latency/energy overheads inherent in STT-RAM caches. Our analysis also reveals that wearable workloads have low cache variability needs, and their cache blocks exhibit short and stable lifetimes. Against the background of this analysis, we explore the tradeoffs of STT-RAM cache architecture designs for the wearable workloads. Specifically, we explore a simple adaptable design that aims to optimize latency or energy, based on runtime needs, without introducing significant design or area overhead. Our analysis shows that STT-RAM caches offer much promise for energy- and area-efficient wearable computing, without introducing much performance overheads.
KW - Adaptable hardware
KW - Internet of Things
KW - Low-power embedded systems
KW - Retention time
KW - Spin-transfer torque RAM (STT-RAM)
KW - Wearable computing
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U2 - 10.1016/j.future.2022.05.023
DO - 10.1016/j.future.2022.05.023
M3 - Article
AN - SCOPUS:85132744648
SN - 0167-739X
VL - 136
SP - 231
EP - 240
JO - Future Generation Computer Systems
JF - Future Generation Computer Systems
ER -