Design activity -- constructing an artifact description satisfying given
goals and constraints -- distinguishes humanity from other animals and
traditional machines, and endowing machines with design abilities at the human
level or beyond has been a long-term pursuit. Though machines have already
demonstrated their abilities in designing new materials, proteins, and computer
programs with advanced artificial intelligence (AI) techniques, the search
space for designing such objects is relatively small, and thus, "Can machines
design like humans?" remains an open question. To explore the boundary of
machine design, here we present a new AI approach to automatically design a
central processing unit (CPU), the brain of a computer, and one of the world's
most intricate devices humanity have ever designed. This approach generates the
circuit logic, which is represented by a graph structure called Binary
Speculation Diagram (BSD), of the CPU design from only external input-output
observations instead of formal program code. During the generation of BSD,
Monte Carlo-based expansion and the distance of Boolean functions are used to
guarantee accuracy and efficiency, respectively. By efficiently exploring a
search space of unprecedented size 10^{10^{540}}, which is the largest one of
all machine-designed objects to our best knowledge, and thus pushing the limits
of machine design, our approach generates an industrial-scale RISC-V CPU within
only 5 hours. The taped-out CPU successfully runs the Linux operating system
and performs comparably against the human-designed Intel 80486SX CPU. In
addition to learning the world's first CPU only from input-output observations,
which may reform the semiconductor industry by significantly reducing the
design cycle, our approach even autonomously discovers human knowledge of the
von Neumann architecture.

通过机器学习方法成功设计并构造了一种微处理器电路，并且性能可以媲美由人类设计的电路。该方法甚至自主发现了冯・诺伊曼结构的人类知识。

机器设计技术的新突破：自动化 CPU 设计与 AI

Pushing the Limits of Machine Design: Automated CPU Design with AI

Modern computation based on the von Neumann architecture is today a mature
cutting-edge science. In the Von Neumann architecture, processing and memory
units are implemented as separate blocks interchanging data intensively and
continuously. This data transfer is responsible for a large part of the power
consumption. The next generation computer technology is expected to solve
problems at the exascale with 1018 calculations each second. Even though these
future computers will be incredibly powerful, if they are based on von Neumann
type architectures, they will consume between 20 and 30 megawatts of power and
will not have intrinsic physically built-in capabilities to learn or deal with
complex data as our brain does. These needs can be addressed by neuromorphic
computing systems which are inspired by the biological concepts of the human
brain. This new generation of computers has the potential to be used for the
storage and processing of large amounts of digital information with much lower
power consumption than conventional processors. Among their potential future
applications, an important niche is moving the control from data centers to
edge devices.
The aim of this Roadmap is to present a snapshot of the present state of
neuromorphic technology and provide an opinion on the challenges and
opportunities that the future holds in the major areas of neuromorphic
technology, namely materials, devices, neuromorphic circuits, neuromorphic
algorithms, applications, and ethics. The Roadmap is a collection of
perspectives where leading researchers in the neuromorphic community provide
their own view about the current state and the future challenges. We hope that
this Roadmap will be a useful resource to readers outside this field, for those
who are just entering the field, and for those who are well established in the
neuromorphic community. this https URL

该研究报告介绍了神经形态计算技术的现状、挑战和机遇，并探讨其在材料、设备、电路、算法、应用和伦理等领域的未来应用发展，旨在为读者提供有价值的资源。

2022 神经形态计算与工程路线图

2022 Roadmap on Neuromorphic Computing and Engineering

We extend the capabilities of neural networks by coupling them to external
memory resources, which they can interact with by attentional processes. The
combined system is analogous to a Turing Machine or Von Neumann architecture
but is differentiable end-to-end, allowing it to be efficiently trained with
gradient descent. Preliminary results demonstrate that Neural Turing Machines
can infer simple algorithms such as copying, sorting, and associative recall
from input and output examples.

本文介绍了神经图灵机的概念及其与外部存储器资源的耦合，通过注意力过程交互，与图灵机或冯・诺伊曼体系结构相似，但可在端到端上进行微分，从而使其能够通过梯度下降进行高效训练。初步结果表明神经图灵机能够从输入和输出示例中推断出简单算法，如复制，排序和关联回忆。