  Resistive-random-access-memory (ReRAM) based processing-in-memory (R$^2$PIM)
accelerators show promise in bridging the gap between Internet of Thing
devices' constrained resources and Convolutional/Deep Neural Networks'
(CNNs/DNNs') prohibitive energy cost. Specifically, R$^2$PIM accelerators
enhance energy efficiency by eliminating the cost of weight movements and
improving the computational density through ReRAM's high density. However, the
energy efficiency is still limited by the dominant energy cost of input and
partial sum (Psum) movements and the cost of digital-to-analog (D/A) and
analog-to-digital (A/D) interfaces. In this work, we identify three
energy-saving opportunities in R$^2$PIM accelerators: analog data locality,
time-domain interfacing, and input access reduction, and propose an innovative
R$^2$PIM accelerator called TIMELY, with three key contributions: (1) TIMELY
adopts analog local buffers (ALBs) within ReRAM crossbars to greatly enhance
the data locality, minimizing the energy overheads of both input and Psum
movements; (2) TIMELY largely reduces the energy of each single D/A (and A/D)
conversion and the total number of conversions by using time-domain interfaces
(TDIs) and the employed ALBs, respectively; (3) we develop an only-once input
read (O$^2$IR) mapping method to further decrease the energy of input accesses
and the number of D/A conversions. The evaluation with more than 10 CNN/DNN
models and various chip configurations shows that, TIMELY outperforms the
baseline R$^2$PIM accelerator, PRIME, by one order of magnitude in energy
efficiency while maintaining better computational density (up to 31.2$\times$)
and throughput (up to 736.6$\times$). Furthermore, comprehensive studies are
performed to evaluate the effectiveness of the proposed ALB, TDI, and O$^2$IR
innovations in terms of energy savings and area reduction.
