Search for: All records

Amber is a system-on-chip (SoC) with a coarse-grained reconfigurable array (CGRA) for acceleration of dense linear algebra applications, such as machine learning (ML), image processing, and computer vision. It is designed using an agile accelerator-compiler co-design flow; the compiler updates automatically with hardware changes, enabling continuous application-level evaluation of the hardware-software system. To increase hardware utilization and minimize reconfigurability overhead, Amber features the following: 1) dynamic partial reconfiguration (DPR) of the CGRA for higher resource utilization by allowing fast switching between applications and partitioning resources between simultaneous applications; 2) streaming memory controllers supporting affine access patterns for efficient mapping of dense linear algebra; and 3) low-overhead transcendental and complex arithmetic operations. The physical design of Amber features a unique clock distribution method and timing methodology to efficiently layout its hierarchical and tile-based design. Amber achieves a peak energy efficiency of 538 INT16 GOPS/W and 483 BFloat16 GFLOPS/W. Compared with a CPU, a GPU, and a field-programmable gate array (FPGA), Amber has up to 3902x, 152x, and 107x better energy-delay product (EDP), respectively. 

Abstract We introduce a notion of ‘cross-section continuity’ as a criterion for the viability of definitions of angular momentum, J , at null infinity: If a sequence of cross-sections, , of null infinity converges uniformly to a cross-section , then the angular momentum, J n , on should converge to the angular momentum, J , on . The Dray–Streubel (DS) definition of angular momentum automatically satisfies this criterion by virtue of the existence of a well defined flux associated with this definition. However, we show that the one-parameter modification of the DS definition proposed by Compere and Nichols—which encompasses numerous other alternative definitions—does not satisfy cross-section continuity. On the other hand, we prove that the Chen–Wang–Yau definition does satisfy the cross-section continuity criterion. 

Electrical neural recordings measured using direct electrical interfaces with neural tissue suffer from a short lifespan because the signal strength decreases over time. The inflammatory response to the inserted microprobe can create insulating tissue over the electrical interfaces, reducing the recorded signal below noise levels. One of the factors contributing to this inflammatory response is the tissue damage caused during probe insertion. Here, we explore the use of ultrasonic actuation of the neural probe during insertion to minimize tissue damage in mice. Silicon neural microprobes were designed and fabricated with integrated electrical recording sites and piezoelectric transducers. The microprobes were actuated at ultrasonic frequencies using integrated piezoelectric transducers. The microprobes were inserted into mouse brains under a glass window over the brain surface to image the tissue surrounding the probe using two-photon microscopy. The mechanical force required to penetrate the tissue was reduced by a factor of 2–3 when the microprobe was driven at ultrasonic frequencies. Tissue histology at the probe insertion site showed a reduced area of damage and decreased microglia counts with increasing ultrasonic actuation of the probes. Two-photon imaging of the microprobe over weeks demonstrated stabilization of the inflammatory response. Recording of electrical signals from neurons over time suggests that microprobes inserted using ultrasound have a higher signal-to-noise ratio over an extended time period.

 

Abstract Accurate and (near) real-time earthquake monitoring provides the spatial and temporal behaviors of earthquakes for understanding the nature of earthquakes, and also helps in regional seismic hazard assessments and mitigations. Because of the increase in both the quality and quantity of seismic data, an automated earthquake monitoring system is needed. Most of the traditional methods for detecting earthquake signals and picking phases are based on analyses of features in recordings of an individual earthquake and/or their differences from background noises. When seismicity is high, the seismograms are complicated, and, therefore, traditional analysis methods often fail. With the development of machine learning algorithms, earthquake signal detection and seismic phase picking can be more accurate using the features obtained from a large amount of earthquake recordings. We have developed an attention recurrent residual U-Net algorithm, and used data augmentation techniques to improve the accuracy of earthquake detection and seismic phase picking on complex seismograms that record multiple earthquakes. The use of probability functions of P and S arrivals and potential P and S arrival pairs of earthquakes can increase the computational efficiency and accuracy of backprojection for earthquake monitoring in large areas. We applied our workflow to monitor the earthquake activity in southern California during the 2019 Ridgecrest sequence. The distribution of earthquakes determined by our method is consistent with that in the Southern California Earthquake Data Center (SCEDC) catalog. In addition, the number of earthquakes in our catalog is more than three times that of the SCEDC catalog. Our method identifies additional earthquakes that are close in origin times and/or locations, and are not included in the SCEDC catalog. Our algorithm avoids misidentification of seismic phases for earthquake location. In general, our algorithm can provide reliable earthquake monitoring on a large area, even during a high seismicity period.