  Non-Volatile Memories (NVMs) such as Resistive RAM (RRAM) are used in
neuromorphic systems to implement high-density and low-power analog synaptic
weights. Unfortunately, an RRAM cell can switch its state after reading its
content a certain number of times. Such behavior challenges the integrity and
program-once-read-many-times philosophy of implementing machine learning
inference on neuromorphic systems, impacting the Quality-of-Service (QoS).
Elevated temperatures and frequent usage can significantly shorten the number
of times an RRAM cell can be reliably read before it becomes absolutely
necessary to reprogram. We propose an architectural solution to extend the read
endurance of RRAM-based neuromorphic systems. We make two key contributions.
First, we formulate the read endurance of an RRAM cell as a function of the
programmed synaptic weight and its activation within a machine learning
workload. Second, we propose an intelligent workload mapping strategy
incorporating the endurance formulation to place the synapses of a machine
learning model onto the RRAM cells of the hardware. The objective is to extend
the inference lifetime, defined as the number of times the model can be used to
generate output (inference) before the trained weights need to be reprogrammed
on the RRAM cells of the system. We evaluate our architectural solution with
machine learning workloads on a cycle-accurate simulator of an RRAM-based
neuromorphic system. Our results demonstrate a significant increase in
inference lifetime with only a minimal performance impact.
