  Modern Systems on Chip (SoC), almost as a rule, require accelerators for
achieving energy efficiency and high performance for specific tasks that are
not necessarily well suited for execution in standard processing units.
Considering the broad range of applications and necessity for specialization,
the design of SoCs has thus become expressively more challenging. In this
paper, we put forward the concept of G-GPU, a general-purpose GPU-like
accelerator that is not application-specific but still gives benefits in energy
efficiency and throughput. Furthermore, we have identified an existing gap for
these accelerators in ASIC, for which no known automated generation
platform/tool exists. Our solution, called GPUPlanner, is an open-source
generator of accelerators, from RTL to GDSII, that addresses this gap. Our
analysis results show that our automatically generated G-GPU designs are
remarkably efficient when compared against the popular CPU architecture RISC-V,
presenting speed-ups of up to 223 times in raw performance and up to 11 times
when the metric is performance derated by area. These results are achieved by
executing a design space exploration of the GPU-like accelerators, where the
memory hierarchy is broken in a smart fashion and the logic is pipelined on
demand. Finally, tapeout-ready layouts of the G-GPU in 65nm CMOS are presented.
