Ayan K. Biswas

Complete magnetization reversal in a magnetostrictive nanomagnet with voltage-generated stress: A reliable energy-efficient non-volatile magneto-elastic memory

Rotating the magnetization of a magnetostrictive nanomagnet with electrically generated strain dissipates far less energy than any other rotation method and would have been the perfect choice for writing bits in non-volatile magnetic memory, except strain cannot ordinarily rotate the magnetization of magnet in a memory cell by more than 90° and “flip” it. Here, we describe a scheme to achieve complete 180° rotation with strain alone without having to precisely time the strain cycle. At room temperature, this writing method results in: (1) energy dissipation <6200 kT per bit, (2) write error probability <10−6, (3) write time of ∼1 ns, and (4) low read error.

Energy-efficient magnetoelastic non-volatile memory

We propose an improved scheme for low-power writing of binary bits in non-volatile (multiferroic) magnetic memory with electrically generated mechanical stress. Compared to an earlier idea [N. Tiercelin et al., J. Appl. Phys. 109, 07D726 (2011)], our scheme improves distinguishability between the stored bits when the latter are read with magneto-tunneling junctions. More importantly, the write energy dissipation and write error rate are reduced significantly if the writing speed is kept the same. Such a scheme could be one of the most energy-efficient approaches to writing bits in magnetic non-volatile memory.

An error-resilient non-volatile magneto-elastic universal logic gate with ultralow energy-delay product

A long-standing goal of computer technology is to process and store digital information with the same device in order to implement new architectures. One way to accomplish this is to use nanomagnetic logic gates that can perform Boolean operations and then store the output data in the magnetization states of nanomagnets, thereby doubling as both logic and memory. Unfortunately, many of these nanomagnetic devices do not possess the seven essential characteristics of a Boolean logic gate : concatenability, non-linearity, isolation between input and output, gain, universal logic implementation, scalability and error resilience. More importantly, their energy-delay products and error rates tend to vastly exceed that of conventional transistor-based logic gates, which is unacceptable. Here, we propose a non-volatile voltage-controlled nanomagnetic logic gate that possesses all the necessary characteristics of a logic gate and whose energy-delay product is two orders of magnitude less than that of other nanomagnetic (non-volatile) logic gates. The error rate is also superior.Magneto-elastic universal logic gate: A non-volatile, error-resilient Boolean logic gate with ultralow energy-delay product Ayan K. Biswas, Jayasimha Atulasimha and Supriyo Bandyopadhyay a) Department of Electrical and Computer Engineering, Virginia Commonwealth University, Richmond, VA 23284, USA Department of Mechanical and Nuclear Engineering, Virginia Commonwealth University, Richmond, VA 23284, USA

Self-Similar Magneto-Electric Nanocircuit Technology for Probabilistic Inference Engines

Probabilistic graphical models are powerful mathematical formalisms for machine learning and reasoning under uncertainty that are widely used for cognitive computing. However, they cannot be employed efficiently for large problems (with variables in the order of 100K or larger) on conventional systems, due to inefficiencies resulting from layers of abstraction and separation of logic and memory in CMOS implementations. In this paper, we present a magnetoelectric probabilistic technology framework for implementing probabilistic reasoning functions. The technology leverages straintronic magneto-tunneling junction (S-MTJ) devices in a novel mixed-signal circuit framework for direct computations on probabilities while enabling in-memory computations with persistence. Initial evaluations of the Bayesian likelihood estimation operation occurring during Bayesian Network inference indicate up to 127× lower area, 214× lower active power, and 70× lower latency compared to an equivalent 45-nm CMOS Boolean implementation.

Architecting for Causal Intelligence at Nanoscale

Conventional Von Neumann microprocessors are inefficient for supporting machine intelligence due to layers of abstraction, limiting the feasibility of machine-learning frameworks in critical applications. A new approach for architecting intelligent systems, using physical equivalence and leveraging emerging nanotechnology, can pave the way to machine intelligence everywhere.

Physically equivalent magneto-electric nanoarchitecture for probabilistic reasoning

Probabilistic machine intelligence paradigms such as Bayesian Networks (BNs) are widely used in critical real-world applications. However they cannot be employed efficiently for large problems on conventional computing systems due to inefficiencies resulting from layers of abstraction and separation of logic and memory. We present an unconventional nanoscale magneto-electric machine paradigm, architected with the principle of physical equivalence to efficiently implement causal inference in BNs. It leverages emerging straintronic magneto-tunneling junctions in a novel mixed-signal circuit framework for direct computations on probabilities, while blurring the boundary between memory and computation. Initial evaluations, based on extensive bottom-up simulations, indicate up to four orders of magnitude inference runtime speedup vs. best-case performance of 100-core microprocessors, for BNs with a million random variables. These could be the target applications for emerging magneto-electric devices to enable capabilities for leapfrogging beyond present day computing.

Design of a Fiber-Optic Sensing Mosquito Trap

Mosquito traps offer researchers and health officials a reasonable estimate of mosquito abundances to assess the spatial and temporal occurrences of mosquito-transmitted pathogens. Existing traps, however, have issued efficient design to detect mosquito and energy consumption of the device. We designed a novel mosquito collection device that sensitively detects the presence of a mosquito via a fiber-optic sensor. In this prototype, a pushing capture mechanism selectively powers and efficiently captures live mosquitoes without destroying identifying morphological features of the specimens. Because the trap sensor selectively powers the capture mechanism, it allows for greatly reduced power consumption when compared with existing continuously operated devices. With appropriate programming, the fans ON and OFF based on the triggering of a fiber-optic sensor detected and counted each mosquito that entered the trap. This trapping platform can be used with a variety of power sources including renewable sources (e.g., solar, wind, or hydroelectric power) in remote settings. The experimental results show a high success ratio 93%-100% for detection of live mosquitoes.

Image Processing With Dipole-Coupled Nanomagnets: Noise Suppression and Edge Enhancement Detection

Hardware-based image processing offers speed and convenience not found in software-centric approaches. Here, we show theoretically that a 2-D periodic array of dipole-coupled elliptical nanomagnets, delineated on a piezoelectric substrate, can act as a dynamical system for specific image processing functions. Each nanomagnet has two stable magnetization states that encode pixel color (black or white). An image containing black and white pixels is first converted to voltage states and then mapped into the magnetization states of a nanomagnet array with magneto-tunneling junctions (MTJs). The same MTJs are employed to read out the processed pixel colors later. Dipole interaction between the nanomagnets implements specific image processing tasks, such as noise reduction and edge enhancement detection. These functions are triggered by applying a global strain to the nanomagnets with a voltage dropped across the piezoelectric substrate. An image containing an arbitrary number of black and white pixels can be processed in few nanoseconds with very low energy cost.

Straintronics: Strain-switched multiferroic nanomagnets for extremely low energy logic/memory

Straintronics is an extraordinarily energy-efficient novel hardware paradigm for digital computing and signal processing. The central idea is to build the basic binary switch with a nanoscale multiferroic consisting of a magnetostrictive layer elastically coupled to a piezoelectric layer. A tiny voltage of a few mV generates sufficient strain in the piezoelectric layer to switch the magnetization of the magnetostrictive nanomagnet for a memory or logic operation, while dissipating few aJ of energy. Low density processors employing straintronics and operating at slow clock speeds of ∼100 MHz are estimated to dissipate such low power that they can run by harvesting energy from the ambient and never even need a battery. We have designed straintronic logic gates and memory cells, and extracted their performance figures with Landau-Lifshitz-Gilbert simulations to predict unprecedented energy efficiency. Rudimentary non-volatile logic and memory functions have been demonstrated experimentally and validate the low energy expectations.

Energy-Efficient Hybrid Spintronic–Straintronic Nonvolatile Reconfigurable Equality Bit Comparator

We propose a reconfigurable bit comparator implemented with a nanowire spin valve whose two contacts aremagnetostrictive with bistable magnetization. Reference and input bits are “written” into the magnetizationstates of the two contacts with electrically generated strain and the spin-valve’s resistance is lowered if theymatch. Multiple comparators can be interfaced in parallel with a magneto-tunneling junction to determineif an N-bit input stream matches an N-bit reference stream bit by bit. The system is robust against thermalnoise at room temperature and a 16-bit comparator can operate at ∼416 MHz while dissipating at most ∼419aJ per cycle.Keywords: Comparator, energy-efficient digital signal processing, straintronics, nanomagnetsDigital signal processing employing electron spins tostore and process bit information offers certain advan-tages over traditional charge-based paradigms