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84                                                                UEC Int’l Mini-Conference No.53







                             An evaluation of multi-core and V extension RISC-V processor

                                     The-Binh NGUYEN*, Trong-Thuc HOANG, Cong-Kha PHAM
                                           Department of Computer Network and Engineering
                                         The University of Electro-Communications, Tokyo, Japan

                   Keywords: Parallel Computing, RISC-V, RISC-V Vector Extension, SIMD


                     1.  Introduction                          The Table 3 shows the number of cycles taken for a single
                     Multi-core  processing  and  SIMD  are  two  orthogonal  yet   image  conversion  and  recognition,  the  speedup  for  simple
                     closely related type of parallel computing. The RISC-V is an   image processing task like color to grayscale image only has
                     open  ISA  that  both  support  multi-core  and  SIMD  via  V   20.77x  speedup  while  the  more  complex  neural  network
                     extension  [1].  This  openness  nature  enables  flexibility  yet   inference, we achieve nearly 300x speedup compared to the
                     difficult to select to optimal configuration for a particular task.   scalar core when the data are already in cache.
                     This work proposes a method to evaluate a RISC-V multi-core
                     or RISC-V with V extension.
                     2.  Method
                     For  the  multi-core  RISC-V  SoC,  we  utilize  the  Chipyard
                     framework  to  generate  various  of  RISC-V  multi-core
                     configuration on the VC707 target FPGA evaluation board. The
                     benchmark that we used are Coremark [2] and Gunrock [3]
                     graph-coloring algorithm.






                                                                    Figure 2: Handwritten hiragana recognition
                                                               4.  Conclusion
                                                               From the benchmarking result, we can see that for some
                                                               workload  that  are  task-based  such  as  the  Coremark  or
                                                               Gunrock algorithm, the multi-core system does provide the
                     For the RISC-V with Vector Extension we implement a simple   promising speedup. However, for the data-based workload,
                     multi-cycle processor with Zve32x sub extension, the vector   providing  the  processor  with  the  new  extension  might
                     length  VLEN  is  128-bit.  Regarding  evaluation,  we  use  an
                     image processing task and neural network inference, namely   improve the performance significantly without the cost of
                     color to grayscale and handwritten letter, respectively.   more complex bus and system architecture to handle the
                     3.  Results                               multi-processing.
                     The Table 1 and Table 2 shows the Gunrock and Coremark   References
                     benchmark result on a RV64GC with varying number of cores   [1] RISC-V “V” Vector Extension,
                     from single-core to octa-core under the Linux operating system.   https://github.com/riscvarchive/riscv-v-spec/blob/master/v
                     The Coremark seems to scale linearly yet the Gunrock limits at   spec.adoc
                     5.84x for octa-core.                      [2] Coremark benchmark, https://github.com/eembc/coremark
                                                               [3] A. Borione, L. Cardone, A. Calabrese and S. Quer, "An
                                                               Experimental Evaluation of Graph Coloring Heuristics on
                                                               Multi- and Many-Core Architectures," in IEEE Access, vol.
                                                               11, pp. 125226-125243, 2023,
                                                               https://ieeexplore.ieee.org/document/10304117



                     *The author is supported by SESS MEXT Scholarship
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