Energy-efficient hybrid spintronic-straintronic reconfigurable bit comparator

We propose a reconfigurable bit comparator implemented with a nanowire spin valve whose two contacts are magnetostrictive with bistable magnetization. Reference and input bits are "written" into the magnetization states of the two contacts with electrically generated strain and the spin-valve's resistance is lowered if they match. Multiple comparators can be interfaced in parallel with a magneto-tunneling junction to determine if an N-bit input stream matches an N-bit reference stream bit by bit. The system is robust against thermal noise at room temperature and a 16-bit comparator can operate at roughly 416 MHz while dissipating at most 420 aJ per cycle.Comment: Submitted to Applied Physics Letters. Version 1 ignored the energy dissipation in the passive resistors since they were very high. However, high resistances increase the RC time constant associated with charging. In version 2, the RC time constant has been reduced at the expense of increased energy dissipation, but the latter is still very small in absolute term

Experimental demonstration of complete 180 degree reversal of magnetization in isolated Co-nanomagnets on a PMN-PT substrate with voltage generated strain

Rotating the magnetization of a shape anisotropic magnetostrictive nanomagnet with voltage-generated stress/strain dissipates much less energy than most other magnetization rotation schemes, but its application to writing bits in non-volatile magnetic memory has been hindered by the fundamental inability of stress/strain to rotate magnetization by full 180 degrees. Normally, stress/strain can rotate the magnetization of a shape anisotropic elliptical nanomagnet by only up to 90 degrees, resulting in incomplete magnetization reversal. Recently, we predicted that applying uniaxial stress sequentially along two different axes that are not collinear with the major or minor axis of the elliptical nanomagnet will rotate the magnetization by full 180 degrees. Here, we demonstrate this complete 180 degree rotation in elliptical Co-nanomagnets (fabricated on a piezoelectric substrate) at room temperature. The two stresses are generated by sequentially applying voltages to two pairs of shorted electrodes placed on the substrate such that the line joining the centers of the electrodes in one pair intersects the major axis of a nanomagnet at ~+30 degrees and the line joining the centers of the electrodes in the other pair intersects at ~ -30 degrees. A finite element analysis has been performed to determine the stress distribution underneath the nanomagnets when one or both pairs of electrodes are activated, and this has been approximately incorporated into a micromagnetic simulation of magnetization dynamics to confirm that the generated stress can produce the observed magnetization rotations. This result portends an extremely energy-efficient non-volatile "straintronic" memory technology predicated on writing bits in nanomagnets with electrically generated stress

Exploring Music Genre Classification: Algorithm Analysis and Deployment Architecture

Music genre classification has become increasingly critical with the advent of various streaming applications. Nowadays, we find it impossible to imagine using the artist's name and song title to search for music in a sophisticated music app. It is always difficult to classify music correctly because the information linked to music, such as region, artist, album, or non-album, is so variable. This paper presents a study on music genre classification using a combination of Digital Signal Processing (DSP) and Deep Learning (DL) techniques. A novel algorithm is proposed that utilizes both DSP and DL methods to extract relevant features from audio signals and classify them into various genres. The algorithm was tested on the GTZAN dataset and achieved high accuracy. An end-to-end deployment architecture is also proposed for integration into music-related applications. The performance of the algorithm is analyzed and future directions for improvement are discussed. The proposed DSP and DL-based music genre classification algorithm and deployment architecture demonstrate a promising approach for music genre classification