Temperature-Aware Optimization of Monolithic 3D Deep Neural Network Accelerators
- Resource Type
- Conference
- Authors
- Shukla, Prachi; Nemtzow, Sean S.; Pavlidis, Vasilis F.; Salman, Emre; Coskun, Ayse K.
- Source
- 2021 26th Asia and South Pacific Design Automation Conference (ASP-DAC) Design Automation Conference (ASP-DAC), 2021 26th Asia and South Pacific. :709-714 Jan, 2021
- Subject
- Components, Circuits, Devices and Systems
Couplings
Three-dimensional displays
Random access memory
Energy efficiency
Thermal analysis
System-on-chip
Optimization
- Language
- ISSN
- 2153-697X
We propose an automated method to facilitate the design of energy- efficient Mono3D DNN accelerators with safe on-chip temperatures for mobile systems. We introduce an optimizer to investigate the effect of different aspect ratios and footprint specifications of the chip, and select energy-efficient accelerators under user-specified thermal and performance constraints. We also demonstrate that using our optimizer, we can reduce energy consumption by 1.6× and area by 2× with a maximum of 9.5% increase in latency compared to a Mono3D DNN accelerator optimized only for performance.