  Machine learning inference engine is of great interest to smart edge
computing. Compute-in-memory (CIM) architecture has shown significant
improvements in throughput and energy efficiency for hardware acceleration.
Emerging non-volatile memory technologies offer great potential for instant on
and off by dynamic power gating. Inference engine is typically pre-trained by
the cloud and then being deployed to the filed. There are new attack models on
chip cloning and neural network model reverse engineering. In this paper, we
propose countermeasures to the weight cloning and input-output pair attacks.
The first strategy is the weight fine-tune to compensate the analog-to-digital
converter (ADC) offset for a specific chip instance while inducing significant
accuracy drop for cloned chip instances. The second strategy is the weight
shuffle and fake rows insertion to allow accurate propagation of the
activations of the neural network only with a key.
