Liquid state machine based pattern recognition on FPGA with firing-activity dependent power gating and approximate computing
Liquid state machine based pattern recognition on FPGA with firing-activity dependent power gating and approximate computing
复制标题
FPGA 上基于液态状态机的模式识别，具有与发射活动相关的功率门控和近似计算
DOI：
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
Peng Li
中科院分区：
文献类型：
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作者：
Qian Wang;Youjie Li;Peng Li
本文介绍了一种用于现实世界模式识别问题的脉冲神经网络模型——液态机的FPGA结构和实现。所提出的架构由一个具有固定突触的并行数字存储库和一个由生物学上合理的监督学习规则调节的读出阶段组成。当使用TI46语音语料库（一种广泛采用的语音识别基准）进行评估时，所提出的FPGA神经形态处理器显示出极具竞争力的识别性能，并且比2.3 GHz AMD OpteronTM处理器提供88X的运行时加速。一些关键的设计问题，如液体神经元的互连，突触权的存储和算术块的设计在这项工作中得到解决。更重要的是，研究表明，液体状态机独特的计算结构和固有的弹性可以用于高效的FPGA实现。结果表明，基于发射活动的功率门控和运行时可调精度的近似算法计算可以在不显著影响语音识别性能的情况下降低高达30.2%的功耗和能量消耗。
This paper presents an FPGA architecture and implementation of the Liquid State Machine, a spiking neural network model, for real world pattern recognition problems. The proposed architecture consists of a parallel digital reservoir with fixed synapses, and a readout stage that is tuned by a biologically plausible supervised learning rule. When evaluated using the TI46 speech corpus, a widely adopted speech recognition benchmark, the presented FPGA neuromorphic processors demonstrate highly competitive recognition performance and provide a runtime speedup of 88X over the 2.3 GHz AMD OpteronTM Processor. A number of critical design issues such as interconnection of liquid neurons, storage of synaptic weights and design of arithmetic blocks are addressed in this work. More importantly, it is shown that the unique computational structure and inherent resilience of the liquid state machine can be leveraged for highly efficient FPGA implementation. For t Iiis, it is demonstrated that the proposed firing-activity based power gating and approximate arithmetic computing with runtime adjustable precision can lead to up to 30.2% reduction in power and energy dissipation without greatly impacting speech recognition performance.