Name
Title | ||
|---|---|---|
Re2PIM: A Reconfigurable ReRAM-Based PIM Design for Variable-Sized Vector-Matrix Multiplication |
Abstract | ||
|---|---|---|
ABSTRACTReRAM-based deep neural network (DNN) accelerator shows enormous potential because of ReRAM's high computational-density and power-efficiency. A typical feature of DNNs is that weight matrix size varies across diverse DNNs and DNN layers. However, current ReRAM-based DNN accelerators adopt a fixed-sized compute unit (CU) design, resulting in a dilemma of trading off between throughput and energy-efficiency: when computing large vector-matrix multiplication with small CUs, the overhead of the peripheral circuits is relatively high; when computing small vector-matrix multiplication with large CUs, the low utilization of ReRAM crossbars damages the throughput. In this work, we propose Re2PIM, a reconfigurable ReRAM-based DNN accelerator. Each tile of Re2PIM is composed of reconfigurable units (RUs), which can be reconfigured as vector-vatrix multiplier (VMM), digital-to-analog converter (DAC), or analog shift-and-add (AS+A). We can reconfigure RUs and obtain CUs of various sizes according to the DNN's weight matrices. It hence assures a high energy-efficiency without damaging throughput given various DNN benchmarks. Evaluations on different DNN benchmarks show that Re2PIM can achieve 27×/34×/1.5× and 5.7×/17×/8.2× improvement in energy efficiency and computational throughput respectively compared to the state-of-art accelerators (PRIME / ISAAC / TIMELY). |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1145/3453688.3461494 | GLSVLSI |
DocType | Citations | PageRank |
Conference | 1 | 0.35 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yilong Zhao | 1 | 2 | 3.08 |
| Zhezhi He | 2 | 136 | 25.37 |
| Naifeng Jing | 3 | 152 | 27.07 |
| Xiaoyao Liang | 4 | 585 | 45.81 |
| Li Jiang | 5 | 1 | 0.35 |