MoCA: Memory-Centric, Adaptive Execution for Multi-Tenant Deep Neural
  Networks
Seah Kim Hasan Genc Vadim Vadimovich Nikiforov Krste Asanović Borivoje Nikolić Yakun Sophia Shao
Driven by the wide adoption of deep neural networks (DNNs) across different
application domains, multi-tenancy execution, where multiple DNNs are deployed
simultaneously on the same hardware, has been proposed to satisfy the latency
requirements of different applications while improving the overall system
utilization. However, multi-tenancy execution could lead to undesired
system-level resource contention, causing quality-of-service (QoS) degradation
for latency-critical applications. To address this challenge, we propose MoCA,
an adaptive multi-tenancy system for DNN accelerators. Unlike existing
solutions that focus on compute resource partition, MoCA dynamically manages
shared memory resources of co-located applications to meet their QoS targets.
Specifically, MoCA leverages the regularities in both DNN operators and
accelerators to dynamically modulate memory access rates based on their latency
targets and user-defined priorities so that co-located applications get the
resources they demand without significantly starving their co-runners. We
demonstrate that MoCA improves the satisfaction rate of the service level
agreement (SLA) up to 3.9x (1.8x average), system throughput by 2.3x (1.7x
average), and fairness by 1.3x (1.2x average), compared to prior work.