Designers must account for the effect of error and imprecision on DNN behavior, especially since these characteristics of DNN can be leveraged to improve performance and energy. Ares presents a fault-injection framework for estimating the resilience of DNNs to permanent hardware faults. DeepN-JPEG revisits JPEG quantization in order to improve classification accuracy when using compressed images. ThUnderVolt enables voltage underscaling of DNN accelerators by tolerating timing errors. Loom presents an accelerator that exploits the variable precision required by different layers of a CNN, increasing performance by reducing precision.