Sungju Ryu

Jangho Baik, Sunghyun Kim, Gisan Ji et al.

Recommendation system has gained a large popularity for a variety of personalized suggestion tasks, but the ever-increasing number of user data makes real-time processing of recommendation systems difficult. NAND flash memory-based in-storage computing scheme can be one of favorable candidates among the various acceleration approaches because the flash memory typically has a larger memory capacity than the other memory types, so it can efficiently handle a large amount of user data for the recommendation inference services. However, different from other neural network applications where data is sequentially fetched from memory, the recommendation system shows the irregular random memory access pattern. Hence, most of the data loaded from the NAND flash array to the page buffer are not used, so a large portion of the internal bandwidth is underutilized, which degrades the performance on the inference acceleration of the recommendation tasks. In this paper, we propose RecFlash, a fast recommendation inference accelerator utilizing a data remapping algorithm with NAND flash-based in-storage computing (ISC). The experimental results show that our proposed method improves the latency and energy consumption by up to 81% and 91.9%, respectively, over the existing NAND flash-based ISC architecture.

Hyunsung Yoon, Sungju Ryu, Jae-Joon Kim

As the size of Deep Neural Networks (DNNs) increases dramatically to achieve high accuracy, the DNNs require a large amount of computations and memory footprint. Pruning, which produces a sparse neural network, is one of the solutions to reduce the computational complexity of neural network processing. To maximize the performance of the computations with such compressed data, dedicated sparse neural network accelerators have been introduced, but complex circuits for matching the indices of non-zero inputs/weights cause large overhead in area and power of processing elements (PEs). The sparse PE becomes significantly larger than the dense PE, which raises an interesting question for designers; "Given the area, isn't it better to use larger number of dense PEs despite the low utilization in sparse matrix computations?" In this paper, we show that the answer is "yes", and demonstrate an area and energy-efficient method for sparse neural network computing on dense-matrix multiplication hardware accelerators (Sparse-on-Dense).

Hyungjun Kim, Yulhwa Kim, Sungju Ryu et al.

Significant computational cost and memory requirements for deep neural networks (DNNs) make it difficult to utilize DNNs in resource-constrained environments. Binary neural network (BNN), which uses binary weights and binary activations, has been gaining interests for its hardware-friendly characteristics and minimal resource requirement. However, BNN usually suffers from accuracy degradation. In this paper, we introduce "BitSplit-Net", a neural network which maintains the hardware-friendly characteristics of BNN while improving accuracy by using multi-bit precision. In BitSplit-Net, each bit of multi-bit activations propagates independently throughout the network before being merged at the end of the network. Thus, each bit path of the BitSplit-Net resembles BNN and hardware friendly features of BNN, such as bitwise binary activation function, are preserved in our scheme. We demonstrate that the BitSplit version of LeNet-5, VGG-9, AlexNet, and ResNet-18 can be trained to have similar classification accuracy at a lower computational cost compared to conventional multi-bit networks with low bit precision (<= 4-bit). We further evaluate BitSplit-Net on GPU with custom CUDA kernel, showing that BitSplit-Net can achieve better hardware performance in comparison to conventional multi-bit networks.