论文信息 - Hot Chips 2020 Posters

Hot Chips 2020 Posters

The emergence of data parallel architectures have enabled new opportunities to address the power limitations and scalability of multi-core processors, allowing new ways to exploit the abundant data parallelism present in emerging big-data and machine learning applications. This transition is getting a significant boost with the advent of RISC-V with its unique modular and extensible ISA, allowing a widerange of low-cost processor designs. In this work, we present Vortex, a full-stack RISC-V GPGPU processor with OpenCL support. The Vortex platform is highly customizable and scalable with a complete open-source compiler, driver, and runtime software stack to enable research in GPU architectures. We evaluated this design using 15 nm technology. We also show the preliminary performance and energy numbers of running them with a subset of benchmarks from the Rodinia Benchmark suite.

[1] Joel Silberman,et al. A Scalable Multi- TeraOPS Deep Learning Processor Core for AI Trainina and Inference , 2018, 2018 IEEE Symposium on VLSI Circuits.

[2] Hoi-Jun Yoo,et al. 7.7 LNPU: A 25.3TFLOPS/W Sparse Deep-Neural-Network Learning Processor with Fine-Grained Mixed Precision of FP8-FP16 , 2019, 2019 IEEE International Solid- State Circuits Conference - (ISSCC).

[3] Hoi-Jun Yoo,et al. A 1.32 TOPS/W Energy Efficient Deep Neural Network Learning Processor with Direct Feedback Alignment based Heterogeneous Core Architecture , 2019, 2019 Symposium on VLSI Circuits.

[4] Hoi-Jun Yoo,et al. 7.4 GANPU: A 135TFLOPS/W Multi-DNN Training Processor for GANs with Speculative Dual-Sparsity Exploitation , 2020, 2020 IEEE International Solid- State Circuits Conference - (ISSCC).

[5] Chia-Hsiang Yang,et al. A 2.25 TOPS/W Fully-Integrated Deep CNN Learning Processor with On-Chip Training , 2019, 2019 IEEE Asian Solid-State Circuits Conference (A-SSCC).

[6] Seungkyu Choi,et al. A 47.4µJ/epoch Trainable Deep Convolutional Neural Network Accelerator for In-Situ Personalization on Smart Devices , 2019, 2019 IEEE Asian Solid-State Circuits Conference (A-SSCC).