  Arbitrary-precision integer multiplication is the core kernel of many
applications in simulation, cryptography, etc. Existing acceleration of
arbitrary-precision integer multiplication includes CPUs, GPUs, FPGAs, and
ASICs. Among these accelerators, FPGAs are promised to provide both good energy
efficiency and flexibility. Surprisingly, in our implementations, FPGA has the
lowest energy efficiency, i.e., 0.29x of the CPU and 0.17x of the GPU with the
same generation fabrication. Therefore, key questions arise: Where do the
energy efficiency gains of CPUs and GPUs come from? Can reconfigurable
computing do better? If can, how to achieve that?
  We identify that the biggest energy efficiency gains of the CPUs and GPUs
come from the dedicated vector units. FPGA uses DSPs and lookup tables to
compose the needed computation, which incurs overhead when compared to using
vector units directly. New reconfigurable computing, e.g., 'FPGA+vector units'
is a novel and feasible solution to improve energy efficiency. In this paper,
we propose to map arbitrary-precision integer multiplication onto such a
heterogeneous platform, i.e., AMD/Xilinx Versal ACAP architecture. Designing on
Versal ACAP incurs several challenges and we propose AIM: Arbitrary-precision
Integer Multiplication on Versal ACAP to automate and optimize the design. AIM
framework includes design space exploration and AIM automatic code generation
to facilitate the system design and verification. We deploy the AIM framework
on three different applications, including large integer multiplication (LIM),
RSA, and Mandelbrot, on the AMD/Xilinx Versal ACAP VCK190 evaluation board. Our
experimental results show that AIM achieves up to 12.6x, and 2.1x energy
efficiency gains over the Intel Xeon Ice Lake 6346 CPU, and NVidia A5000 GPU
respectively, which brings reconfigurable computing the most energy-efficient
platform among CPUs and GPUs.
