NanoMagnet Logic: an Architectural Viewpoint

Among the possible implementation of Field- Coupled devices NanoMagnet Logic is attractive for its low power consumption and the possibility to combine memory and logic in the same device. However, the nature of these technologies is so different from CMOS transistors that the implications on the circuit architecture must be taken carefully into account. In this work we analyze the most important issues related to the design of complex circuits using this technology. We discuss how they influence the architectural level. We propose detailed solutions to solve these problems and to improve the overall performance. As a result of this analysis the type of circuits and applications that constitute the best target for this technology are identified. The analysis is performed on NanoMagnet Logic but the results can be applied to any QCA technolog

NanoMagnet Logic: an Architectural Viewpoint

Among the possible implementation of Field- Coupled devices NanoMagnet Logic is attractive for its low power consumption and the possibility to combine memory and logic in the same device. However, the nature of these technologies is so different from CMOS transistors that the implications on the circuit architecture must be taken carefully into account. In this work we analyze the most important issues related to the design of complex circuits using this technology. We discuss how they influence the architectural level. We propose detailed solutions to solve these problems and to improve the overall performance. As a result of this analysis the type of circuits and applications that constitute the best target for this technology are identified. The analysis is performed on NanoMagnet Logic but the results can be applied to any QCA technolog

Biosequences analysis on NanoMagnet Logic

In the last decade Quantum dot Cellular Automata technology has been one of the most studied among the emerging technologies. The magnetic implementation, NanoMagnet Logic (NML), is particularly interesting as an alternative solutions to CMOS technology. The main advantages of NML circuits resides in the possibility to mix logic and memory in the same device, the expected low power consumption and the remarkable tolerance to heat and radiations. NML and QCA circuits behavior is different w.r.t. their CMOS counterparts. Consequently architecture organization must be tailored to their characteristics, and it is important to identify which applications are best suited for this technology. Our contribution reported in this paper represents a considerable step-forward in this direction. We present an optimized implementation on NML technology of an hardware accelerator for biosequences analysis. The architecture leverages the systolic array structure, which is the best organization for this technology due to the regularity of the layout. The circuit is described using a VHDL model, simulated to verify the correct functionality from the application point of view, and performance are evaluated, both in terms of speed and power consumption. Results pinpoints that NML technology with the appropriate clock solution can reach a considerable reduction in power consumption over CMOS. This analysis highlights quantitatively, and not only qualitatively, that NML logic is perfectly suited for Massively Parallel Data Analysis applications

Interleaving in Systolic-Arrays: a Throughput Breakthrough

In past years the most common way to improve computers performance was to increase the clock frequency. In recent years this approach suffered the limits of technology scaling, therefore computers architectures are shifting toward the direction of parallel computing to further improve circuits performance. Not only GPU based architectures are spreading in consideration, but also Systolic Arrays are particularly suited for certain classes of algorithms. An important point in favor of Systolic Arrays is that, due to the regularity of their circuit layout, they are appealing when applied to many emerging and very promising technologies, like Quantum-dot Cellular Automata and nanoarrays based on Silicon NanoWire or on Carbon nanotube Field Effect Transistors. In this work we present a systematic method to improve Systolic Arrays performance exploiting Pipelining and Input Data Interleaving. We tackle the problem from a theoretical point of view first, and then we apply it to both CMOS technology and emerging technologies. On CMOS we demonstrate that it is possible to vastly improve the overall throughput of the circuit. By applying this technique to emerging technologies we show that it is possible to overcome some of their limitations greatly improving the throughput, making a considerable step forward toward the post-CMOS era

Heterogeneous Reconfigurable Fabrics for In-circuit Training and Evaluation of Neuromorphic Architectures

A heterogeneous device technology reconfigurable logic fabric is proposed which leverages the cooperating advantages of distinct magnetic random access memory (MRAM)-based look-up tables (LUTs) to realize sequential logic circuits, along with conventional SRAM-based LUTs to realize combinational logic paths. The resulting Hybrid Spin/Charge FPGA (HSC-FPGA) using magnetic tunnel junction (MTJ) devices within this topology demonstrates commensurate reductions in area and power consumption over fabrics having LUTs constructed with either individual technology alone. Herein, a hierarchical top-down design approach is used to develop the HSCFPGA starting from the configurable logic block (CLB) and slice structures down to LUT circuits and the corresponding device fabrication paradigms. This facilitates a novel architectural approach to reduce leakage energy, minimize communication occurrence and energy cost by eliminating unnecessary data transfer, and support auto-tuning for resilience. Furthermore, HSC-FPGA enables new advantages of technology co-design which trades off alternative mappings between emerging devices and transistors at runtime by allowing dynamic remapping to adaptively leverage the intrinsic computing features of each device technology. HSC-FPGA offers a platform for fine-grained Logic-In-Memory architectures and runtime adaptive hardware. An orthogonal dimension of fabric heterogeneity is also non-determinism enabled by either low-voltage CMOS or probabilistic emerging devices. It can be realized using probabilistic devices within a reconfigurable network to blend deterministic and probabilistic computational models. Herein, consider the probabilistic spin logic p-bit device as a fabric element comprising a crossbar-structured weighted array. The Programmability of the resistive network interconnecting p-bit devices can be achieved by modifying the resistive states of the array\u27s weighted connections. Thus, the programmable weighted array forms a CLB-scale macro co-processing element with bitstream programmability. This allows field programmability for a wide range of classification problems and recognition tasks to allow fluid mappings of probabilistic and deterministic computing approaches. In particular, a Deep Belief Network (DBN) is implemented in the field using recurrent layers of co-processing elements to form an n x m1 x m2 x ::: x mi weighted array as a configurable hardware circuit with an n-input layer followed by i ≥ 1 hidden layers. As neuromorphic architectures using post-CMOS devices increase in capability and network size, the utility and benefits of reconfigurable fabrics of neuromorphic modules can be anticipated to continue to accelerate

Energy and Area Efficient Machine Learning Architectures using Spin-Based Neurons

Recently, spintronic devices with low energy barrier nanomagnets such as spin orbit torque-Magnetic Tunnel Junctions (SOT-MTJs) and embedded magnetoresistive random access memory (MRAM) devices are being leveraged as a natural building block to provide probabilistic sigmoidal activation functions for RBMs. In this dissertation research, we use the Probabilistic Inference Network Simulator (PIN-Sim) to realize a circuit-level implementation of deep belief networks (DBNs) using memristive crossbars as weighted connections and embedded MRAM-based neurons as activation functions. Herein, a probabilistic interpolation recoder (PIR) circuit is developed for DBNs with probabilistic spin logic (p-bit)-based neurons to interpolate the probabilistic output of the neurons in the last hidden layer which are representing different output classes. Moreover, the impact of reducing the Magnetic Tunnel Junction\u27s (MTJ\u27s) energy barrier is assessed and optimized for the resulting stochasticity present in the learning system. In p-bit based DBNs, different defects such as variation of the nanomagnet thickness can undermine functionality by decreasing the fluctuation speed of the p-bit realized using a nanomagnet. A method is developed and refined to control the fluctuation frequency of the output of a p-bit device by employing a feedback mechanism. The feedback can alleviate this process variation sensitivity of p-bit based DBNs. This compact and low complexity method which is presented by introducing the self-compensating circuit can alleviate the influences of process variation in fabrication and practical implementation. Furthermore, this research presents an innovative image recognition technique for MNIST dataset on the basis of p-bit-based DBNs and TSK rule-based fuzzy systems. The proposed DBN-fuzzy system is introduced to benefit from low energy and area consumption of p-bit-based DBNs and high accuracy of TSK rule-based fuzzy systems. This system initially recognizes the top results through the p-bit-based DBN and then, the fuzzy system is employed to attain the top-1 recognition results from the obtained top outputs. Simulation results exhibit that a DBN-Fuzzy neural network not only has lower energy and area consumption than bigger DBN topologies while also achieving higher accuracy

Colloquium: Ice rule and emergent frustration in particle ice and beyond

Geometric frustration and the ice rule are two concepts that are intimately connected and widespread across condensed matter. The first refers to the inability of a system to satisfy competing interactions in the presence of spatial constraints. The second, in its more general sense, represents a prescription for the minimization of the topological charges in a constrained system. Both can lead to manifolds of high susceptibility and non-trivial, constrained disorder where exotic behaviors can appear and even be designed deliberately. In this Colloquium, we describe the emergence of geometric frustration and the ice rule in soft condensed matter. This Review excludes the extensive developments of mathematical physics within the field of geometric frustration, but rather focuses on systems of confined micro- or mesoscopic particles that emerge as a novel paradigm exhibiting spin degrees of freedom. In such systems, geometric frustration can be engineered artificially by controlling the spatial topology and geometry of the lattice, the position of the individual particle units, or their relative filling fraction. These capabilities enable the creation of novel and exotic phases of matter, and also potentially lead towards technological applications related to memory and logic devices that are based on the motion of topological defects. We review the rapid progress in theory and experiments and discuss the intimate physical connections with other frustrated systems at different length scales

Annual Report 2019 - Institute of Ion Beam Physics and Materials Research

The Institute of Ion Beam Physics and Materials Research conducts materials research for future applications in, e.g., information technology. To this end, we make use of the various possibilities offered by our Ion Beam Center (IBC) for synthesis, modification, and analysis of thin films and nanostructures, as well as of the free-electron laser FELBE at HZDR for THz spectroscopy. The analyzed materials range from semiconductors and oxides to metals and magnetic materials. They are investigated with the goal to optimize their electronic, magnetic, optical as well as structural functionality. This research is embedded in the Helmholtz Association’s programme “From Matter to Materials and Life”. Seven publications from last year are highlighted in this Annual Report to illustrate the wide scientific spectrum of our institute. After the scientific evaluation in the framework of the Helmholtz Programme-Oriented Funding (POF) in 2018 we had some time to concentrate on science again before end of the year a few of us again had to prepare for the strategic evaluation which took place in January 2020, which finally was also successful for the Institute

Annual Report 2020 - Institute of Ion Beam Physics and Materials Research

As for everybody else also for the Institute of Ion Beam Physics and Materials Research (IIM), the COVID-19 pandemic overshadowed the usual scientific life in 2020. Starting in March, home office became the preferred working environment and the typical institute life was disrupted. After a little relaxation during summer and early fall, the situation became again more serious and in early December we had to severely restrict laboratory activities and the user operation of the Ion Beam Center (IBC). For the most part of 2020, user visits were impossible and the services delivered had to be performed hands-off. This led to a significant additional work load on the IBC staff. Thank you very much for your commitment during this difficult period. By now user operation has restarted, but we are still far from business as usual. Most lessons learnt deal with video conference systems, and everybody now has extensive experience in skype, teams, webex, zoom, or any other solution available. Conferences were cancelled, workshops postponed, and seminar or colloquia talks delivered online. Since experimental work was also impeded, maybe 2020 was a good year for writing publications and applying for external funding. In total, 204 articles have been published with an average impact factor of about 7.0, which both mark an all-time high for the Institute. 13 publications from last year are highlighted in this Annual Report to illustrate the wide scientific spectrum of our institute. In addition, 20 new projects funded by EU, DFG, BMWi/AiF and SAB with a total budget of about 5.7 M€ have started. Thank you very much for making this possible. Also, in 2020 there have been a few personalia to be reported. Prof. Dr. Sibylle Gemming has left the HZDR and accepted a professor position at TU Chemnitz. Congratulations! The hence vacant position as the head of department was taken over by PD Dr. Artur Erbe by Oct. 1st. Simultaneously, the department has been renamed to “Nanoelectronics”. Dr. Alina Deac has left the institute in order to dedicate herself to new opportunities at the Dresden High Magnetic Field Laboratory. Dr. Matthias Posselt went to retirement after 36 years at the institute. We thank Matthias for his engagement and wish him all the best for the upcoming period of his life. However, also new equipment has been setup and new laboratories founded. A new 100 kV accelerator is integrated into our low energy ion nanoengineering facility and complements our ion beam technology in the lower energy regime. This setup is particularly suited to perform ion implantation into 2D materials and medium energy ion scattering (MEIS). Finally, we would like to cordially thank all partners, friends, and organizations who supported our progress in 2020. First and foremost we thank the Executive Board of the Helmholtz-Zentrum Dresden-Rossendorf, the Minister of Science and Arts of the Free State of Saxony, and the Ministers of Education and Research, and of Economic Affairs and Energy of the Federal Government of Germany. Many partners from univer¬sities, industry and research institutes all around the world contributed essentially, and play a crucial role for the further development of the institute. Last but not least, the directors would like to thank all members of our institute for their efforts in these very special times and excellent contributions in 2020

Chirality as Generalized Spin-Orbit Interaction in Spintronics

This review focuses on the chirality observed in the excited states of the magnetic order, dielectrics, and conductors that hold transverse spins when they are evanescent. Even without any relativistic effect, the transverse spin of the evanescent waves are locked to the momentum and the surface normal of their propagation plane. This chirality thereby acts as a generalized spin-orbit interaction, which leads to the discovery of various chiral interactions between magnetic, phononic, electronic, photonic, and plasmonic excitations in spintronics that mediate the excitation of quasiparticles into a single direction, leading to phenomena such as chiral spin and phonon pumping, chiral spin Seebeck, spin skin, magnonic trap, magnon Doppler, and spin diode effects. Intriguing analogies with electric counterparts in the nano-optics and plasmonics exist. After a brief review of the concepts of chirality that characterize the ground state chiral magnetic textures and chirally coupled magnets in spintronics, we turn to the chiral phenomena of excited states. We present a unified electrodynamic picture for dynamical chirality in spintronics in terms of generalized spin-orbit interaction and compare it with that in nano-optics and plasmonics. Based on the general theory, we subsequently review the theoretical progress and experimental evidence of chiral interaction, as well as the near-field transfer of the transverse spins, between various excitations in magnetic, photonic, electronic and phononic nanostructures at GHz time scales. We provide a perspective for future research before concluding this article.Comment: 136 pages, 60 figure