  The last decade has seen the emergence of a new generation of multi-core in
response to advances in machine learning, and in particular Deep Neural Network
(DNN) training and inference tasks. These platforms, like the JETSON AGX
XAVIER, embed several cores and accelerators in a SWaP- efficient (Size Weight
and Power) package with a limited set of resources. However, concurrent
applications tend to interfere on shared resources, resulting in high execution
time variability for applications compared to their behaviour in
isolation.Access control techniques aim to selectively restrict the flow of
operations executed by a resource. To reduce the impact of interference on the
JETSON Volta GPU, we specify and implement an access control technique to
ensure each GPU operation executes in isolation to reduce its timing
variability. We implement the controller using three different strategies and
assess their complexity and impact on the application performance. Our
evaluation shows the benefits of adding the access control: its transparency to
applications, reduced timing variability, isolation between GPU operations, and
small code complexity. However, the strategies may cause some potential
slowdowns for applications even in isolation but which are reasonable.
