  The effectiveness of deep neural networks (DNN) in vision, speech, and
language processing has prompted a tremendous demand for energy-efficient
high-performance DNN inference systems. Due to the increasing memory intensity
of most DNN workloads, main memory can dominate the system's energy consumption
and stall time. One effective way to reduce the energy consumption and increase
the performance of DNN inference systems is by using approximate memory, which
operates with reduced supply voltage and reduced access latency parameters that
violate standard specifications. Using approximate memory reduces reliability,
leading to higher bit error rates. Fortunately, neural networks have an
intrinsic capacity to tolerate increased bit errors. This can enable
energy-efficient and high-performance neural network inference using
approximate DRAM devices.
  Based on this observation, we propose EDEN, a general framework that reduces
DNN energy consumption and DNN evaluation latency by using approximate DRAM
devices, while strictly meeting a user-specified target DNN accuracy. EDEN
relies on two key ideas: 1) retraining the DNN for a target approximate DRAM
device to increase the DNN's error tolerance, and 2) efficient mapping of the
error tolerance of each individual DNN data type to a corresponding approximate
DRAM partition in a way that meets the user-specified DNN accuracy
requirements.
  We evaluate EDEN on multi-core CPUs, GPUs, and DNN accelerators with error
models obtained from real approximate DRAM devices. For a target accuracy
within 1% of the original DNN, our results show that EDEN enables 1) an average
DRAM energy reduction of 21%, 37%, 31%, and 32% in CPU, GPU, and two DNN
accelerator architectures, respectively, across a variety of DNNs, and 2) an
average (maximum) speedup of 8% (17%) and 2.7% (5.5%) in CPU and GPU
architectures, respectively, when evaluating latency-bound DNNs.
