A distributed nanocluster based multi-agent evolutionary network

Abstract

As an important approach of distributed artificial intelligence, multi-agent system provides an efficient way to solve large-scale computational problems through high-parallelism processing with nonlinear interactions between the agents. However, the huge capacity and complex distribution of the individual agents make it difficult for efficient hardware construction. Here, we propose and demonstrate a multi-agent hardware system that deploys distributed Ag nanoclusters as physical agents and their electrochemical dissolution, growth and evolution dynamics under electric field for high-parallelism exploration of the solution space. The collaboration and competition between the Ag nanoclusters allow information to be effectively expressed and processed, which therefore replaces cumbrous exhaustive operations with self-organization of Ag physical network based on the positive feedback of information interaction, leading to significantly reduced computational complexity. The proposed multi-agent network can be scaled up with parallel and serial integration structures, and demonstrates efficient solution of graph and optimization problems. An artificial potential field with superimposed attractive/repulsive components and varied ion velocity is realized, showing gradient descent route planning with self-adaptive obstacle avoidance. This multi-agent network is expected to serve as a physics-empowered parallel computing hardware.

Neuromorphic Artificial Vision Systems Based on Reconfigurable Ion‐Modulated Memtransistors

“…[16,22] Nowadays, many researchers put forward the idea of reconfiguring device's functions on the same hardware platform. [15,[24][25][26] One of the studies made use of reconfigurable synaptic and neuronal functions in the V/VO x / HfWO x /Pt memristors for spiking neural network, [24] manipulating the ion distributions in HfWO x memristors to enable devices working on different modes.…”

Neuromorphic Artificial Vision Systems Based on Reconfigurable Ion-modulated Memtransistors

“…[15,21] Nowadays, many researchers put forward the idea of reconfiguring device's functions on the same hardware platform. [14,[23][24][25] One of the studies made use of reconfigurable synaptic and neuronal functions in the V/VO x /HfWO x /Pt memristors for spiking neural network, [23] manipulating the ion distributions in HfWO x memristors to enable devices working on different modes.…”

Neural Architecture Search with In‐Memory Multiply–Accumulate and In‐Memory Rank Based on Coating Layer Optimized C‐Doped Ge2Sb2Te5 Phase Change Memory

“…Memristor technology has been widely used for advanced computing architectures, such as in-memory computing [8][9][10][11] and neuromorphic computing. [12][13][14][15] By utilizing device characteristics and array-level organization and scheduling, a large number of operations can be completed within or near the memory, effectively reducing the data transmission overhead between the memory and the processor. [16][17][18] Recently, in-memory computing technology based on in-memory MAC [19][20][21] has aroused extensive attention.…”