Research projects

I have been associated with the following projects since my graduate studies in the EECS department at University of California, Berkeley.

  1. Challenges in TV White Space operations in India
    Students: Gaurang Naik, Naireeta Kansabanik, Sudesh Singhal, Garima Maheshwari, and Meghna Khaturia
    Research staff: Rajeev Paniyar
    TV white space operation has picked up by regulators and technology companies in the United States and United Kingdom in the past decade. Overall, some exciting times are ahead with Super-WiFi and IEEE 802.11af type of technologies on the horizon. Not much work has been done in the Indian context for TV white spaces. India consists of one of the fastest growing telecommunication market in the world. Exploration and innovation of technologies suitable for white space or unlicensed operation in the TV band in India is the focus of our work.
  2. Spatial sampling, quantization, and reconstruction of physical fields
    Students: Akshta Athawale, Kaushani Majumder, Ankur Mallick, Abhinav Kumar, and Ajinkya Jayawant
    Summer students: Alankrita Bhatt (IIT Kanpur, BTech), Nikita Vasudevan (LNMIIT Jaipur, BTech)
    Remote sensing of physical phenomenon by an array of sensors is of interest. Since spatially distributed physical fields cannot be prefiltered, usual techniques like "prefiltering" from centralized sampling setup cannot be used. Using these natural constraints, a host of sampling problems can be explored. In one such problem, we have explored the A/D conversion of bounded dynamic-range two-dimensional smooth non-bandlimited fields. We provide upper-bounds on pointwise error between the field and its reconstruction in terms of the spectral properties of the field. More problems are under exploration.
  3. Oversampling and ADC precision tradeoffs in sampling of signals
    Students: Akshta Athawale, Aishwarya Goyal, T. V. Srikanth, Ayush Baid, and Ashray Malhotra
    Summer students: Swati Vyas (IIT Guwahati, BTech)
    In any signal A/D conversion process, bits are essential. For reducing signal values into bits, analog to digital converters (ADCs) are essential. This process is called as quantization. Each ADC is accompanied by a precision, which determines how accurate the quantization process is. Lack of quantization precision can be compensated by suitably designed oversampling technique. This opens avenues for research problems related to (design of) sampling or A/D conversion schemes of signals. One would expect ADC precision to tradeoff against oversampling ratio. Such tradeoffs are the subject of examination of this research.
  4. SRAM reliability models for bit-level failures
    Students: Amrut Kolhapure, Sreeja Vasantham, Gautam Kapila, and Sonal Gupta
    With technology scaling, static random access memory (SRAM) leakage power is expected to consume a large fraction of the total power budget. Reduction of the SRAM supply-voltage, to reduce this leakage power, is impeded by reliability concerns arising due to various failure mechanisms: (i) soft-errors, (ii) oxide-trap induced current fluctuations, (iii) parametric failures due to process variations, and (iv) supply-voltage noise. These failures are transient in nature, and hence these failures will couple with each other. Currently, separate supply-voltage margins are provisioned for each of these failure mechanisms to ensure smooth operation of SRAM within some target reliability. This work aims to explore and understand bit-error probability of SRAM cell as a function of the supply voltage.
  5. System-level power-optimization in SRAM: a reliability perspective
    Advisors: Jan Rabaey and Kannan Ramchandran
    Reduction of the SRAM supply-voltage, to reduce the leakage power, is impeded by reliability concerns arising due to various failure mechanisms: (i) soft-errors, (ii) oxide-trap induced current fluctuations, (iii) parametric failures due to process variations, and (iv) supply-voltage noise. A statistical or probabilistic setup is used to model failure mechanisms like soft-errors or process-variations, and error-probability of stored data is used as a metric for reliability. Error models which combine various SRAM cell failure mechanisms are developed. In a probabilistic setup, the bit-error probability increases due to supply voltage reduction, but it can be reduced by suitable choices of error-correction code and data-refresh (scrubbing) rate. The leakage-power including redundancy overhead, coding power, and data-refresh power is set as the cost-function and an error-probability target is set as the constraint. The cost-function is minimized subject to the constraint, over the choices of data-refresh rate, error-correction code, and supply voltage. The optimization procedure is evaluated using failure-rate simulations for 90nm and 65nm CMOS technology.
  6. Error-correction code detection from observed noisy binary data
    Students: Arti Yardi, Ashok Vardhan, and Sai Bhargav
    For cognitive radio networks, it is helpful for the secondary to have a knowledge of the codebook of the primary. Otherwise, in security or military applications, a third party may want to discover the error-correction code being used in communication. In this work, schemes to detect or estimate the error-correction code being used by a transmitter is being studied. Of particular interest is the case when data is affected by a binary symmetric channel, thereby making the observed data as noisy.

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