您将学到什么：
- 比较数字计算与模拟计算
- What strides have been taken to make analog a better alternative to digital?
- 神经网络有多重要。
In 1974, Theodore Nelson, the inventor of hypertext, wrote in his book “Computer Lib/Dream” that “analog computers are so unimportant compared to digital computers that we will polish them off in a couple of paragraphs.” This popular attitude toward analog computing hasn’t shifted much in the decades since then, despite the incredible advances made in analog computing technology.
与数字相比，模拟的计算速度和功率效率已经有望长期存在。这个问题是开发模拟系统一直被许多障碍所困扰，包括模拟处理器的规模和成本。物联网的爆炸和AI应用的增长引起了人们对开发模拟计算的新方法的兴趣，以解决与日益复杂的工作负载相关的一些挑战。
Edge AI applications need to be low-cost, small-form-factor devices with low latency, high performance, and low power（见图）。模拟解决方案为这些挑战提供了非常有说服力的解决方案，这可能会让许多人感到惊讶。模拟技术的最新进展，再加上使用非易失性记忆（如闪存记忆），消除了传统的障碍。
What follows are 11 common myths associated with analog computing.
1.数字计算比模拟计算更好。
数字计算解决方案已经迎来了信息时代，并将曾经是房间大小的计算机变成了令人难以置信的强大机器，这些机器可以适合我们手掌。可以公平地说，长期以来，数字计算解决方案优于大多数应用程序的模拟解决方案。但是，时间已经改变，当我们查看未来的需求时（每个设备都将在边缘配备强大的AI）时，很明显，数字计算将无法跟上。通过模拟计算，通常需要大型，渴望的GPU的算法将在一个可以集成到任何设备中的小型，低功能，具有成本效益的芯片上运行。
2. Moore’s Law will continue scaling.
如今，只有少数制造商可以遵循摩尔的法律趋势（1990年代的数十个），这太过于成本过高。过程节点的改进减慢了，而制造成本却大幅上升。简而言之，对于摩尔的法律规模，它不再像往常一样。下一代AI处理需要新的方法。
3.模拟系统太复杂而无法设计。
Modern electronic-design-automation (EDA) tools have come a long way to enable high-speed simulation of analog circuits with a high level of fidelity. In addition, the ability for analog circuits to automatically calibrate and compensate for error has progressed by leaps and bounds. This calibration technology allows designers to build analog compute systems modularly and not worry about how other parts of the system affect the analog circuits.
4. Analog compute is mainly a research effort.
在1950年代和1960年代，模拟计算机开始在商业应用中过时，尽管模拟计算仍在研究研究以及某些工业和军事应用中使用。当然，从那以后发生了很多变化。公司喜欢Mythicare taking analog processors to production, proving that analog is not only viable for commercial applications, but also offers an optimized solution for the computing challenges of AI today and in the future.
5. Analog systems aren’t capable of high performance.
模拟电路可能会非常快，因为它们不需要依靠逻辑通过数字逻辑门传播，也不需要从内存库中删除的数字值。通过使用通过闪存记忆阵列引导的微型电流，可以在不到一微秒的情况下进行大规模平行的矩阵操作。
这种性能使模拟系统非常适合计算密集型工作负载，例如使用对象检测，分类和深度估计的视频分析应用程序。这些功能对于工业机器视觉，自动无人机，监视摄像头和网络视频记录器（NVR）应用程序非常有用。
6.模拟是渴望的。
雷达下的一个问题是，数字系统被迫以DRAM存储神经网络，这是一种昂贵，不便和渴望的方法。DRAM在积极使用和闲置时期都消耗了大量功率，因此系统架构师花费大量时间和精力来最大程度地利用处理器。
数字系统的另一个问题是它们非常精确，这在性能和力量上以巨大的成本，尤其是在中性网络方面。只需考虑一个必须从3D非易失性存储器的大堆栈中读取数万亿个权重的系统，以立即计算AI算法。
实际上，人工智能不需要这种精确度。In fact, some analog processors, such as Mythic’s Analog Matrix Processor, which perform analog compute inside of very dense non-volatile memory, are already up to 10X more energy-efficient than digital systems (with the potential to be 100X to 1000X more energy-efficient for certain use cases). They’re also much faster and can pack 8X more information into the memory. One big advantage of analog being more energy-efficient is that it can support extremely high processing densities without the need for advanced cooling or power-supply infrastructure, which is particularly important for industrial and enterprise applications.
7.模拟芯片在设计和制造上很昂贵。
There has long been a perception that analog is much more expensive to design and manufacture than digital systems. However, the truth is that it’s becoming increasingly difficult for digital systems to keep up with the increasing costs of manufacturing and mask-set prices, which can reach beyond $100 million for the 1- to 3-nm range. These costs must be amortized, making it harder to achieve improvements in functionality per dollar. For digital systems to keep up with the growing computing demands of the AI industry, everything on the chip would need to realize massive performance, cost, and power improvements.
Analog systems offer a host of performance and power advantages, while also being incredibly cost-efficient. This is because high performance and incredible memory density can be achieved on older process nodes with analog compute. These process nodes are significantly lower cost in terms of mask sets and wafer prices, are mature and stable, and have far greater manufacturing capacity compared to bleeding-edge nodes
8.模拟系统（例如数字系统） - 必须在DRAM中存储神经网络。
硬件最重要的方面之一是，每毫米平方可以将多少内存包装到处理器中，以及内存汲取了多少功率。对于数字系统而言，主流记忆（SRAM和DRAM）旨在消耗太多功率，占用太多芯片区域，并且没有足够快地改进，无法推动当今AI时代所需的改进。
模拟系统具有能够使用非易失性存储器（NVM）的优点，该内存提供了令人印象深刻的密度并解决了功率泄漏问题。某些模拟系统采用闪存，这是NVM最常见的类型之一，因为它具有令人难以置信的密度，与硬盘驱动器相比很小，并且可以保留无功率的信息。使用模拟计算中的计算，算术是通过操纵和组合小电流在NVM细胞内部执行的，这些电流以快速和低功率的方式在整个存储库中进行。
9. Analog can’t run complex deep neural networks.
Conventional digital processing systems support complex deep neural networks (DNNs). The problem is that these platforms take up considerable silicon real estate, require DRAM, and consume lots of energy, which is why many AI applications offload most of the deep-learning work to remote cloud servers. For systems that require real-time processing for DNNs, the data must be processed locally.
当模拟计算与Flash技术结合使用时，处理器可以在片上运行多个大型，复杂的DNN。这消除了对DRAM芯片的需求，并在单芯片加速器内实现了令人难以置信的致密重量存储。处理器可以通过并行运行许多计算元素来进一步最大化推理性能。随着对实时处理的需求不断增长，这种复杂DNN模型的芯片执行将变得越来越关键。
10. Analog systems aren’t as compact as digital systems.
It’s true that analog systems have traditionally been far too big. However, new approaches make it possible to design incredibly compact systems. One reason is the high density of flash, so by combining analog compute with flash memory, it’s possible to use a single flash transistor as a storage medium, and multiplier, and an adder (accumulator) circuit.
11.模拟系统对不断变化的环境条件没有弹性。
数字的一种优势是，它对改变环境条件（例如温度变化和供应电压的变化）具有广泛的容忍度。在过去的模拟系统中，电压的任何微小变化都可能导致处理时错误。
但是，某些方法可以使模拟对不同的环境条件具有相同的弹性并进行大规模交付。大多数现代模拟电路都是软件控制的，并使用了一系列补偿和校准技术。结果，它们可以在有时表现出高度差异的现代数字过程中制造。这些技术还可以弥补不断变化的温度和电压，这使现代的高速模拟电路能够在我们所有电子设备中发挥关键作用。
