  The primary operation in DNNs is the dot product of quantized input
activations and weights. Prior works have proposed the design of memory-centric
architectures based on the Processing-In-Memory (PIM) paradigm. Resistive RAM
(ReRAM) technology is especially appealing for PIM-based DNN accelerators due
to its high density to store weights, low leakage energy, low read latency, and
high performance capabilities to perform the DNN dot-products massively in
parallel within the ReRAM crossbars. However, the main bottleneck of these
architectures is the energy-hungry analog-to-digital conversions (ADCs)
required to perform analog computations in-ReRAM, which penalizes the
efficiency and performance benefits of PIM. To improve energy-efficiency of
in-ReRAM analog dot-product computations we present ReDy, a hardware
accelerator that implements a ReRAM-centric Dynamic quantization scheme to take
advantage of the bit serial streaming and processing of activations. The energy
consumption of ReRAM-based DNN accelerators is directly proportional to the
numerical precision of the input activations of each DNN layer. In particular,
ReDy exploits that activations of CONV layers from Convolutional Neural
Networks (CNNs), a subset of DNNs, are commonly grouped according to the size
of their filters and the size of the ReRAM crossbars. Then, ReDy quantizes
on-the-fly each group of activations with a different numerical precision based
on a novel heuristic that takes into account the statistical distribution of
each group. Overall, ReDy greatly reduces the activity of the ReRAM crossbars
and the number of A/D conversions compared to an static 8-bit uniform
quantization. We evaluate ReDy on a popular set of modern CNNs. On average,
ReDy provides 13\% energy savings over an ISAAC-like accelerator with
negligible accuracy loss and area overhead.
