A Pedagogical Model to Integrate Computational Thinking Logic to First Year Design Studio

Today, computational thinking and computational design approaches transform almost all stages of architectural practice and education. In this context, since students are most likely to encounter computers, in this study, the approach of teaching students computational design logic is adopted instead of teaching how to use computers only as a drafting or representation tool. This study focuses on developing a pedagogical model that aims to teach computational thinking logic and analog computing through a design process. The proposed model consists of four modules as follows: abstraction of music and text (Module 1), decomposition of buildings (Module 2), analysis of body-space (Module 3), design of a space by the help of spatial patterns (Module 4). The proposed model is applied to first-year students in Interior Design Studio in the 2019-2020 fall semester. As a result of Module 4, students designed both anticipated and unanticipated spaces in an algorithmic way.

Approximate analog computing with metatronic circuits

Analog photonic solutions offer unique opportunities to address complex computational tasks with unprecedented performance in terms of energy dissipation and speeds, overcoming current limitations of modern computing architectures based on electron flows and digital approaches. The lack of modularization and lumped element reconfigurability in photonics has prevented the transition to an all-optical analog computing platform. Here, we explore, using numerical simulation, a nanophotonic platform based on epsilon-near-zero materials capable of solving in the analog domain partial differential equations (PDE). Wavelength stretching in zero-index media enables highly nonlocal interactions within the board based on the conduction of electric displacement, which can be monitored to extract the solution of a broad class of PDE problems. By exploiting the experimentally achieved control of deposition technique through process parameters, used in our simulations, we demonstrate the possibility of implementing the proposed nano-optic processor using CMOS-compatible indium-tin-oxide, whose optical properties can be tuned by carrier injection to obtain programmability at high speeds and low energy requirements. Our nano-optical analog processor can be integrated at chip-scale, processing arbitrary inputs at the speed of light.

Resonance for analog recurrent neural network

There is a strong interest in using physical waves for artificial neural computing because of their unique advantages in fast speed and intrinsic parallelism. Resonance, as a ubiquitous feature across many wave systems, is a natural candidate for analog computing in temporal signals. We demonstrate that resonance can be used to construct stable and scalable recurrent neural networks. By including resonators with different lifetimes, the computing system develops both short-term and long-term memory simultaneously.

High-speed single perceptron for optical neural networks based on microcombs

Optical artificial neural networks (ONNs) — analog computing hardware tailored for machine learning [1, 2] — have significant potential for ultra-high computing speed and energy efficiency [3]. We propose a new approach to architectures for ONNs based on integrated Kerr micro-comb sources [4] that is programmable, highly scalable and capable of reaching ultra-high speeds. We experimentally demonstrate the building block of the ONN — a single neuron perceptron — by mapping synapses onto 49 wavelengths of a micro-comb to achieve a high single-unit throughput of 11.9 Giga-FLOPS at 8 bits per FLOP, corresponding to 95.2 Gbps. We test the perceptron on simple standard benchmark datasets — handwritten-digit recognition and cancer-cell detection — achieving over 90% and 85% accuracy, respectively. This performance is a direct result of the record small wavelength spacing (49GHz) for a coherent integrated microcomb source, which results in an unprecedented number of wavelengths for neuromorphic optics. Finally, we propose an approach to scaling the perceptron to a deep learning network using the same single micro-comb device and standard off-the-shelf telecommunications technology, for high-throughput operation involving full matrix multiplication for applications such as real-time massive data processing for unmanned vehicle and aircraft tracking.

A Cluster of FPAAs to Recognize Images using Neural Networks

Analog computing has been recovering its relevance in the recent years. FPAAs are the equivalent to FPGAs but in the analog domain. The main drawback of FPAAs is their reduced integration capacity. In order to increase the amount of analog resources, in this paper a cluster of 40 FPAAs is proposed. As a use case, a 19-8-6-4 feedforward Neural Network has been implemented on such cluster. With the help of a DCT-based software framework, this NN is able to classify 28x28 MNIST images. Results show that the analog network is able to obtain almost the same results as the software baseline network.<br>