United States

Today, essentially all computing is done with Boolean logic and binary numbers, requiring all manner of error correction codes to keep the most significant bits straight. But in the realm of machine learning, what if instead every bit was equally (in)significant? Consider two randomly generated strings thousands of zeros and ones long, about 50/50 split. Normally, about half the bits will be different between the two strings. If the number of differences is less than half though, the strings as a whole must be related. Even a large number of errant bits will not change the strings’ similarity. This creation and measurement of similarity between very large strings of bits (or numbers) is called hyperdimensional computing (HDC). 

Though not quantum mechanical, a kind of superposition of information across each of the thousands of bits enables machine learning capabilities even Deep Neural Networks (DNN) struggle with, such as learning from a single example or combining the learning from several different sensor modalities. One can even dynamically reprogram swarms of robots in a contested environment – the new program intelligible from a single binary string even with 30-50% noise.

What if we started training artificial neural networks (ANN) to learn and recall these strings: not memorizating or autoencoding but rather “resonating” like a tuning fork? This “resonance” training paradigm suddenly enables even more capabilities not possible with traditional DNNs today such as learning new categories “on the fly” without retraining the whole network from scratch. Such attractor state networks (ASN) lend themselves to modular design, allowing new categories to be added or shared amongst other ASNs. This combining of HDC with “resonance” ANNs is a potential game changer for machine learning, establishing mathematically rigorous engineering design rules instead of the unintelligible black boxes of DNNs today.

Hyperdimensional computing can be implemented in traditional computing hardware, but large size, weight, and power savings are possible by including memristors crossbars into the ML ensemble. The Pipeline for Novel Neuromorphic Hardware (NeuroPipe) Applied Research for Advancement of Priorities (ARAP) is working to bring not only novel neuromorphic computing hardware like memristors to the field but also develop the next generation of machine learning algorithms such as these.


NAML 2022

NeuroPipe ARAP Hardware &amp; Algorithms

attractor state networks

hyperdimensional computing

memristors

memristor

artificial neural networks

[![](https://assets.underline.io/uploads/markdown_image/1/image/985c8a40b04e56675221c8549975de3c.png)](https://drive.google.com/file/d/1B70nPIeeeqdG4UU70NlYXuKVOHLRuJEo/view?usp=sharing)

### ** History of DRC**
The Device Research Conference (DRC) began in the pre-transistor era with the 1942 establishment of the Conference on Electron Device Research (CEDR). Following William Shockley’s 1948 CEDR presentation of the transistor device, the Solid-State Device Research Conference (SSDRC) was established, with invitation-only attendance.

In the ensuing decades, many exciting firsts were presented at SSDRC, including the use of impurity diffusion in silicon and germanium, the first practical MOSFET, and the first lattice-matched heterojunction.

In 1969, under the leadership or Herbert Kroemer and Cal Quate, the CEDR and SSDRC merged into DRC. In 1970, attendance was opened to all, and the first DRAM (dynamic random-access memory), room temperature lasers, and first CCD (charge-coupled device) were reported—all in the same 1970 DRC meeting!

1976 saw the first official coordination of tandem meetings between DRC and the Electronic Materials Conference (EMC) to bring together the electronic materials and device communities—a custom which persists to this day. In 1991, DRC began publishing its digest with IEEE; they can all be found [here on IEEE Xplore](https://ieeexplore.ieee.org/xpl/conhome/1000199/all-proceedings). Throughout this period, DRC featured reports of many novel and record-setting devices, including the HEMT (High Electron Mobility Transistor), VCSEL (Vertical Cavity Surface Emitting Laser), HBT (Heterojunction Bipolar Transistor), and nanoscale MOSFETs.

In recent years, DRC has continued to be the world’s premier forum to present innovative semiconductor device research with work on organic thin film transistors, graphene and carbon nanotube transistors, and 2D materials all featuring prominently in the 1990s through to the present day. In 2017, we celebrated DRC’s 75th anniversary with special sessions and an [article in the Journal of the Electron Device Society](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8168249&isnumber=8232486). DRC technical topics continue to evolve with emerging device trends such as memory devices, quantum devices, optoelectronics/photonics, and wide bandgap power devices to reflect the exciting work being done by a diverse community of device engineers around the globe.

Today, as we look forward to the next 75+ years of device innovation, DRC continues to lead the way in showcasing top-caliber research, facilitating vibrant discussion and debate and acting as a platform to nurture the next generation of device engineers. We welcome you to join us at the next DRC!

To learn more about DRC's history, please see [Aaron Franklin, Debdeep Jena and Deji Akinwande, "75 Years of the Device Research Conference—A History Worth Repeating," in IEEE Journal of the Electron Devices Society, vol. 6, pp. 116-120, 2018. doi: 10.1109/JEDS.2017.2780778](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8168249&isnumber=8232486).

### **Topics**
* Biological Devices
* Devices for Extreme Conditions
* Heterogeneously Integrated Devices
* Memory Devices
* Modeling and Simulation of Devices
* Nanoscale and Vacuum Devices
* Optoelectronic and Optical Devices
* Other Emerging Devices
* Power Devices
* Quantum Devices
* RF and Terahertz Devices
* Spintronic and Magnetic Devices
* Thin-film Devices
* Wide-bandgap Devices
 
### **Invited Speakers**
* Patrick Fay, University of Notre Dame
* Susanne Hoffmann-Eifert, Peter Grünberg Institute
* Sanghyeon Kim, Korea Advanced Institute of Science and Technology
* Sriram Krishnamoorthy, University of California, Santa Barbara
* Mario Lanza, King Abdullah University of Science and Technology
* Xiuling Li, The University of Texas at Austin
* Marko Radosavljevic, Intel Corporation
* Siddharth Rajan, The Ohio State University
* Giordano Scappucci, Delft University of Technology
* Fisher Yu, University of Arkansas
* Wenjuan Zhu, University of Illinois at Urbana-Champaign




Registration is required to attend this event. If you have not already done so, please use the link below to register for a ticket to the conference:

https://www.mrs.org/drc-2022/registration

Registration Is Required

DRC 2022

DRC continues to be the world’s premier forum to present innovative semiconductor device research with work on organic thin film transistors, graphene and carbon nanotube transistors, and 2D materials all featuring prominently in the 1990s through to the present day. In 2017, we celebrated DRC’s 75th anniversary with special sessions and an article in the Journal of the Electron Device Society. 
DRC technical topics continue to evolve with emerging device trends such as memory devices, quantum devices, optoelectronics/photonics, and wide bandgap power devices to reflect the exciting work being done by a diverse community of device engineers around the globe.

NeuroPipe ARAP Hardware & Algorithms

technical paper

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Naval Information Warfare Center Pacific is partnering with AFCEA San Diego chapter to host the workshop.

The NAML workshop is an opportunity for researchers from Navy research and development centers and our partner organizations to exchange technical ideas and to come together as a team.

The goals of NAML are:

To spread awareness of current machine learning research relevant to Naval applications

To connect machine learning researchers with experts in military requirements

To build and strengthen collaborations within the defense research community


<figure class="video_container">
  <iframe src="https://s3.amazonaws.com/pf-user-files-01/u-59356/uploads/2022-03-22/hk13mxo/NAML_ReceptionWithtitle.mp4" width=826  height=526  frameborder="0" allowfullscreen="true"> </iframe>
</figure>

Naval Information Warfare Center Pacific is partnering with AFCEA San Diego chapter to host the workshop. The NAML workshop is an opportunity for researchers from Navy research and development centers and our partner organizations to exchange technical ideas and to come together as a team.

The goals of NAML are:

To spread awareness of current machine learning research relevant to Naval applications

To connect machine learning researchers with experts in military requirements

To build and strengthen collaborations within the defense research community



In this paper we study the multi-agent pursuit-evasion problem, and present an extension of the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) deep reinforcement learning algorithm. Previous pursuit-evasion advancements with MADDPG have focused on training capture strategies dependent on the restriction of evader movement with environmental features. We demonstrate a method to train pursuer agents to collaboratively surround and encircle an evader for reliable capture without a strategy rooted in environment entrapment (i.e. cornering). Our method utilizes a novel two-stage, variable-aggression, continuous reward function based on geometrical inscribed circles (incircles), along with a corresponding observation space, with agents operating in an entrapment-disadvantaged environment. Our results show reliable capture of an intelligent, superior evader by three trained pursuers in open space with our encircling strategy.

Robotic Swarm Pursuit-Evasion Capture Strategy using Deep Reinforcement Learning - An Update

In this work, we study a cognitive radio network in which the objective is to maximize the throughput of a secondary user (SU) subject to a constraint on the collision probability with the primary user (PU). For a Markov transmission model of the PU, it has been shown that Age of Information (AoI) plays an important role in this optimization. We have previously developed a throughput-optimal secondary user sensing/transmission policy by solving the linear program for the optimal distribution of the PU state and the AoI of sensed information. This approach requires that the PU idle/transmission dynamics are known, which is not typically the case in practice. Therefore, we implement this approach within a reinforcement learning algorithm by learning the model and then applying the optimization to the learned model. Reinforcement learning algorithms are typically only concerned with maximizing rewards, and therefore do not account for the constraint of avoiding collisions with the PU. To control this cost as well as the reward, we apply a model-based reinforcement learning approach for constrained Markov Decision Processes (CMDP) based on Upper Confidence Reinforcement Learning (UCRL-CMDP), which has proven regret guarantees for the reward and cost. We compare this approach with a model-based (Dyna-Q) and a model-free (Actor-Critic) RL algorithm with a Lagrangian reward function to incorporate the constraint. Simulation results demonstrate that the UCRL-CMDP performs favorably compared with the other two approaches in terms of throughput and computational efficiency.

Online Learning of the Secondary User Policy in a Cognitive Radio Network with Constraints on a Markov Primary User with Unknown Dynamics

Mitigation of range-restricted communication blocks in a distributed swarm is of interest to the Navy, due to the existence of data sharing limitations in all types of sensors and missions carried out in communications-denied environments. If a distributed swarm is affected by a communications block or delay, individual members should still be able to cooperatively identify, evaluate, and act as a team to preserve mission success based on a number of critical parameters guiding the scenario. In our approach, individual members of a heterogeneous swarm in a communication blocked environment construct their own perceived Common Operating Picture (COP). We modified a previously proposed task allocation algorithm to account for incomplete member COPs, and implemented a communications-limiting algorithm in simulation space that emulates a real-world communications block. This block impacts the decision making and asset prioritization of the task allocation algorithm, and our work contributes to a better understanding of task allocation and swarm regrouping in a limited communications environment. Results suggest that compensating for a swarm’s incomplete COP in a limited communications environment achieves a higher likelihood of mission success, and that a quasi-centralized approach for task dissemination ultimately performed more reliably than a fully decentralized approach.

Performative Analysis of Sensor Limitations in Communications Denied Autonomous Swarms

Tactical mission planning for air combat missions is a human-intensive procedure to determine how to coordinate a diverse set of assets. This coordination becomes increasingly difficult due to the physical constraints of the system and the multiple time scales. This paper borrows from safety-critical control to formulate a mathematical description of a class of tactical mission plans. This mathematical description results in a Hamilton-Jacobi-Isaacs (HJI) partial differential equation (PDE). However, due to the curse of dimensionality, numerical solutions to this high dimensional PDE are computationally intensive. To produce a numerical solution, the HJI equation is solved using deep learning. This formulation illustrates the effectiveness and utility of differential games for mission planning through the solution of two games. The first game, the Air3D game, is a collision avoidance scenario between two vehicles. The second is the active target defense (ATD) game. An infinite time solution has been recently found in the controls literature. This paper determines a finite time approximation that is compared with the infinite time solution. In particular, the region of space that the defender protects in a finite time horizon is numerically estimated.

Misson Planning Using Differential Games and Deep Learning

Fast reorientation of space-based sensors is needed to increase ISR capacity and to respond to high-value targeting requests. Rapid reorientation maneuvers are realized by constructing and solving a nonlinear optimization problem that models the physics of the rotational motion as part of the ground process of mission operations. The solution trajectories are then uplinked to the spacecraft for attitude guidance. Attitude guidance commands for a spacecraft are a time-series of incremental rotations that are given to the attitude control system at the closed-loop feedback rate which is typically greater than 10 Hz.

In practical space systems, it is generally not possible to uplink reorientation commands as a finely sampled time-series (at or greater than 10 Hz) since this would quickly exhaust the capacity of a spacecraft’s command buffer. Instead, the current paradigm is to issue a reorientation command as a single time-tagged target orientation. Then, an on-board trajectory generator is used to construct an appropriate rotation command time-series at the closed-loop feedback rate which are then fed to the attitude guidance system. Typically, these on-board trajectory generators do not have the processing power to perform trajectory optimization and instead generate a feasible, but sub-optimal, time-series command set at the appropriate rate.

A potential solution to this problem is to compute and then compress the optimized time-series commands using an autoencoder. An autoencoder is an unsupervised machine learning application of a neural network that conducts a non-linear mapping of the inputs to a lower dimensional space, called the latent space, using the encoder portion of the autoencoder. It then reconstructs the same input from the latent space using the decoder portion of the autoencoder. With such a method, the optimized command sequence can be generated and encoded on the ground, then only the latent space is transmitted to the spacecraft and decoded on-board the spacecraft resulting in the reconstructed optimized rotation sequence at the closed-loop feedback rate. Given the time-series nature of the optimized command sequence, one appropriate architecture for the autoencoder is a long short term memory (LSTM) neural network autoencoder.

In this talk, we demonstrate the methodology for constructing and training the LSTM autoencoder and show the reconstruction error for different degrees of compression represented by decreasing dimensionality of the latent space. We then compare the reconstructed command sequence and the uncompressed time-optimal solutions using a flight simulation software at the closed-loop feedback rate. The result is a new and computationally efficient approach for implementing non-legacy maneuvers on board a spacecraft. There are numerous applications beyond spacecraft where this capability can benefit the DoD such as small unmanned vehicle operations and operations in degraded electromagnetic environments.

A Method to Compress and Transmit Optimal Reorientation Commands to a Spacecraft Using Long Short-Term Memory Neural Network Autoencoders

Current approaches for handling machinery control system (MCS) faults on unmanned surface vessels (USVs) are based on guidance and thresholds selected by subject matter experts. While this can be a robust method, it requires significant human input and validation testing in expected operating conditions. To provide vessels with greater autonomy, data driven approaches for MCS faults are of interest. Data driven approaches based on AI/ML, besides saving both time and money, may provide a constructive means to learn from past experience, learn new insights, and optimally handle faults based on the current environment and conditions (e.g. vessel speed, payload, or sea state). One significant challenge with using AI/ML approaches for fault handling is the issue of class balance – in general, one may have limited realizations or samples of a given fault. In order to positively utilize data from previous successful test events, this work proposes the casting the problem of fault detection as an outlier detection problem. By building a healthy state model, detection of deviations from the nominal state provide a means to recognize new or novel operating conditions or the failure or degradation of a given subsystem. By quantifying and tracking the healthy state, long-term trends and typical behavior can be identified. These metrics could also be used to schedule inspections and maintenance based on the condition of the vessel as opposed to predetermined ones. With most outlier detection methods, typically a given threshold probability needs to be selected. Optimal selection of this threshold requires observations of such an outlier. Given that many of the sensors used to trigger hull, mechanical, and electrical (HM&E) faults are sensors with stochastic components (e.g. temperature, pressure, etc.), this work focuses on providing the probability of observing a given sensor reading or fault based on prior measurements. Quantile regression forests (QRF) provide a means to identify outliers, produces measurement statistics, and handle the randomness associated with HM&E sensors. Given time, other AI/ML approaches may be compared to QRF. As many vessels have separate MCS for each subsystem, data engineering is needed to aggregate, synchronize, and decimate data as a preprocessing step. With no further preprocessing, preliminary work has found QRF to be useful to identity component failure. Future efforts plan to scale the training test set to aid in generating long-term trend and statistics and establishing a healthy baseline.

Application of AI/ML to Improve Unmanned Surface Vessel Machinery Control System Fault Handling

Autonomous systems, including self-driving cars and unmanned underwater/surface/ground/air/space vehicles, have caught the imagination of the press, the public, and personnel of the U.S. Department of Defense (DoD). Moreover, these systems increasingly harness the power of Artificial Intelligence and Machine Learning (AI/ML), more specifically Deep Learning (DL), for their operation. The stunning performance of deep learning algorithms in comparison to extant methods, including pattern matching, computational linguistics, statistical inferencing, and legacy machine-learning, has taken the world by storm. However, adoption of such systems in safety-critical applications has been the subject of intense debate and scrutiny, because of the propensity of these algorithms for erroneous operation, such as misdirection with seemingly innocuous signage, and their vulnerability to data poisoning and data sparsity attacks, leading to incorrect operation which can be both embarrassing and damaging. This has naturally led the DoD community to ask, “How do we harness this technology being unleashed upon the world, and yet keep our nation and warfighters safe?” Before we answer this question, however, it is important to note that trust is integral to DoD systems, including autonomous systems, and ensuring reliable system operations is paramount. Therefore, we need strategies to equip the DoD to design, build, deploy, and sustain autonomous systems that are trustworthy, secure, reliable, and dependable. In this paper, we investigate issues leading to poor performance and lack of robustness of autonomous systems based on machine learning, and discuss the state of the art for their mitigation.

Assuring Autonomous Cyber Physical Data Driven Systems (AA-CPDDS)

Autonomous Systems consist of different systems working together so they can sense, plan, and act to a dynamic environment. There is much confusion on what autonomous systems are. This session will explain what autonomous systems are and how machine teaching and simulation can collaborate to create autonomous systems. Machine Teaching is critical to autonomous systems. It is based on reinforcement learning. The differences between machine teaching and machine learning will also be clarified.

Understanding Autonomous Systems

Reinforcement learning (RL) is an effective method for solving problems that require autonomous agents to learn the best way to act in complex environments. RLlib is a powerful tool for applying reinforcement learning to problems where there are multiple agents or when agents must take on multiple roles. There exist many resources for learning about RLlib from a theoretical or academic perspective, but there is a lack of materials for learning how to use RLlib to solve your own practical problems. This tutorial helps to fill that gap. We show you how to apply reinforcement learning to your problem by taking you through a custom environment and demonstrating how to apply RLlib to that environment. We make the code available on GitHub.

Tutorial: RLlib for Deep Hierarchical Multiagent Reinforcement Learning

Although Convolutional Neural Networks (CNNs) achieve high accuracy on image recognition tasks, they lack robustness against realistic corruptions and fail catastrophically when deliberately attacked. Previous work suggests that CNNs with representations similar to primary visual cortex (V1) are more robust to adversarial attacks on images than current adversarial defense techniques. However, these approaches require training on large-scale neural recordings or handcrafting classical neuroscientific models. Motivated by long-standing evidence that neural activity in V1 and other sensory areas is sparse, we develop a class of hybrid CNNs, called LCANets, which feature a frontend with recurrent lateral competition. We demonstrate competitive accuracy of LCANets on action recognition datasets and show that LCANets are significantly more robust to image corruptions and adversarial examples than both standard CNNs and adversarially-trained CNNs. Our results demonstrate that recurrent lateral competition plays a large role in forming sparse and robust V1-like representations when incorporated into CNNs.

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Robotic Swarm Pursuit-Evasion Capture Strategy using Deep Reinforcement Learning - An Update

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An Experimentally Validated, Universal Memristor Model Enabling Temporal Neuromorphic Computation

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