一个用于节能语音识别和转录的模拟AI芯片
S Ambrogio1, P Narayanan2, A Okazaki3
1IBM Research - Almaden, San Jose, CA, USA. stefano.ambrogio@ibm.com.
Nature
|August 23, 2023
概括
本研究介绍了用于人工智能 (AI) 任务的节能模拟AI芯片. 该芯片实现了高性能和软件等效的准确性,克服了当前AI硬件的局限性.
科学领域:
- 人工智能
- 计算机工程
- 材料科学
背景情况:
- 大型人工智能 (AI) 模型对传统处理器需要大量的能量.
- 模拟内存计算 (模拟人工智能) 为人工智能提供了潜在的能源效率增长.
- 现有的模拟人工智能方法在复杂模型的准确性和相互通信方面存在困难.
研究的目的:
- 开发具有高能效和软件等效的模拟AI芯片.
- 应对大型神经网络的模拟AI扩展的挑战.
- 在模拟人工智能中展示并行处理和通信的新架构.
主要方法:
- 设计和制造了一个模拟AI芯片, 整合了3500万个相变存储器.
- 实现了大规模的并行通讯和低功耗模拟外围电路.
- 将神经网络模型映射到芯片的内存设备上,包括关键字发现和循环神经网络传感器 (RNNT).
主要成果:
- 实现了芯片持续的性能,每秒每瓦高达12.4特拉操作 (TOPS/W).
- 对于关键字检测网络来说,完全证明了端到端软件等效 (SWeq) 的准确性.
- 在MLPerf循环神经网络传感器 (RNNT) 基准测试中使用了五个芯片中的超过1.4亿个内存设备,达到接近SWeq的准确性.
结论:
- 开发的模拟AI芯片为节能AI处理提供了可行的解决方案.
- 该架构成功地展示了复杂的人工智能模型的可扩展性和高精度.
- 这项工作推进了下一代人工智能硬件的模拟计算领域.
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