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Perceptually Guided Inexact DSP Design for Power, Area Efficient Hearing

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Perceptually Guided Inexact DSP Design for Power, Area Efficient Hearing Perceptually Guided Inexact DSP Design for Power, Area Efficient Hearing Aid S. P. Kadiyala, Aritra Sen, Shubham Mahajan, Qingyun Wang, Avinash Lingamneni, James Sneed German, Xu Hong, Ansuman Banerjee, Krishna Palem, Arindam Basu To cite this version: S. P. Kadiyala, Aritra Sen, Shubham Mahajan, Qingyun Wang, Avinash Lingamneni, et al.. Per- ceptually Guided Inexact DSP Design for Power, Area Efficient Hearing Aid. 2015 IEEE Biomed- ical Systems and Circuits Conference (BioCAS), Oct 2015, Atlanta, United States. 10.1109/Bio- CAS.2015.7348319. hal-01498940 HAL Id: hal-01498940 https://hal.archives-ouvertes.fr/hal-01498940 Submitted on 24 Apr 2019 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Perceptually Guided Inexact DSP Design for Power, Area Efficient Hearing Aid Sai Praveen Kadiyala∗, Aritra Sen∗, Shubham Mahajan, Qingyun Wang∗, Avinash Lingamneniy, James Sneed Germanz, Xu Hongz, Ansuman Banerjee, Krishna V. Palemy, and Arindam Basu∗ ∗School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore yDepartment of Computer Science, Rice University, Houston, USA zSchool of Humanities and Social Sciences, Nanyang Technological University, Singapore Email: [email protected] Abstract—Inexact design has been recognized as very viable its kind wherein a neurocognitive model of human hearing approach to achieve significant gains in the energy, area and is used to quantify glitches or loss in accuracy, through a speed efficiencies of digital circuits. By deliberately trading error novel metric of intelligibility of spoken sound. The model in return for such these gains, inexact circuits and architectures have been shown to be especially useful in contexts where and the intelligibility metric are used to guide the design of our senses such as sight and hearing, can compensate for an inexact processor for an assistive device–a hearing aid. the loss in accuracy. It is therefore important to understand, Using the techniques described earlier, we can get reduction characterize the manner in which our sensorial systems interact in the power area product (PAP) of the filter bank by 1:8X and compensate for the loss in accuracy. Further use this for an intelligibility loss of only 10% over conventional exact knowledge to optimize and guide the manner in which inexactness is introduced. For the first time, we achieve both of these goals in designs. this paper in the context of human audition-specifically, using the architecture of a hearing-aid and the DSP primitive of an FIR II. PERCEPTUALLY GUIDED PRUNING FOR EFFICIENT filter as our candidate. Our algorithms for designing an inexact INEXACT CIRCUITS hearing-aid thus use intelligibility as the metric. The resulting Probabilistic Pruning [1], [2] is an inexact design technique inexact FIR filter in the hearing aid is 1:5X or 1:8X more efficient in terms of power-area product while producing 5% that exploits the knowledge of the significance of a circuit or 10% less intelligible speech respectively when compared with component to derive a systematic approach to prune the “least the corresponding exact version. 1 useful” components in a circuit. In this paper, we will use this technique as the basis for introducing “inexactness” and en- I. INTRODUCTION AND MOTIVATION abling the energy-accuracy tradeoffs. The novel contributions Recently, there is a huge interest in developing low power of this paper in the area of pruning is the introduction of an wearable sensors for medical applications and power aware efficient optimization strategy for pruning that is scalable to digital designs are playing a crucial role in this process. large digital systems. Most of the available digital processors however do not take A. Optimization Framework the advantage of information processing done by the natural auditory and visual path ways, hence leaving a scope to Earlier work on pruning [2] has considered the removal dig in this direction. The ability of human compensatory of gates in arithmetic circuits like adders and multipliers by neurocognitive processing to tolerate relatively less accurate using explicit cost functions. However, such approaches are inputs may provide an excellent opportunity to lower the intractable for large systems because of the non-availability of power and area requirement of processors in assistive devices such explicit cost functions and the severe overheads involved by deliberately introducing errors in computation. This novel in determining those cost functions computationally at the approach termed as inexact design has proved to yield signifi- granularity of individual gates. Hence, we propose to have cant gains in the context of hardware for DSP primitives [1], a library E of NE elemental circuits, Ei, each of which can atmospheric modelling [3], MPEG coding [4] and recognition admit only one of several pre-characterized pruned topologies. and classification tasks [5]. These topologies can be indexed by an integer l that denotes However, past work on inexact circuits has quantified the the level or degree of pruning; larger values of l indicate glitches or errors introduced by arithmetic magnitude–as op- more savings at the cost of increased error magnitude [2]. posed to using metrics that capture their impact on our senses Let Li denote an integer that indicates the maximum level of 2 in a natural way. Naturally, incorporating this effect adds to the pruning of Ei E while l = 1 corresponds to the unpruned complexity of design significantly since the human designer structure. Now, we can define a set C of all components cj f g 2 has to bridge the gap between the arithmetic error on the one allowed in our design by denoting cj = l; Ei where Ei E ≤ 2 hand, and its impact on our senses on the other. To remedy and l( Li) N. The circuit we want to optimize can this situation, in this paper, we present the first result of be represented as a directed acyclic graph G whose nodes are NG components selected from C, inputs, or outputs and 1Financial support from MOE, Singapore through grant ARC8/13 is ac- whose edges are wires. We can now formulate an optimization knowledged. problem where the performance of G can be modified by LD Speech Mic Amp ADC WDRC WDRC ∑ WDRC Input speech Hearing loss Hearing Aid model Error estimate Candidate Intelligibility Topology estimate Power/ Area Original Quality Intelligibility estimate Speech Regres sion H Pruner A Psychophysi Stage 1 cal Experiment Fig. 1: Framework for evaluation of cost in the context of a hearing-aid and using that to introduce inexactness in the design. The optimization loop uses a library of pre-characterized inexact VLSI components to quickly achieve a near optimal solution which is then evaluated rigorously through detailed synthesis procedures. ~ • varying L = [l1l2:::lNG ] in exchange of cost savings. To make To give preference to those components which can reduce this dependence explicit, we shall henceforth refer to the graph M without compromising P er too much, we modify the as G(L~ ). cost function to be: The quality of outputs fOg of G(L~ ) for the same set of M inputs fIg depends on the value of L~ with the best quality of C = + λu(P er − P er ); P er =6 0 (3) P er TH outputs being obtained for L~ = [11::1] = L~u corresponding to the unpruned circuit. We formally define the performance where λ is a large positive number to prevent choices ~ as a function of L by: which reduce the performance below threshold and u is Xν the heaviside function. ~ ~ 2 <+ P er = pkQp(Ok(L);Ok(Lu)); P er (1) • To prevent large jumps in error, we only allow changes in k=1 pruning levels of the q(< s) components which have the q where Ok(L~ ) represents the output of G(L~ ) for input Ik largest gradient values by using a ‘rank’ function, rankq that occurs with a probability pk for 1 ≤ k ≤ ν. Qp that assigns values 1; 1=2; ::1=q to the selected ones while denotes a function that measures the perceptual quality of the assigning a value of 0 to others. Hence, the final update output of the pruned circuit with respect to the output of the equation is given by: unpruned one based on models of human sensory processing ~ ~ with larger values denoting better quality. The problem can L(n + 1) = L(n) + rankq(Is(n)rC) (4) now be defined as finding the optimal value of L~ , L~ opt such that: where Is is the identity matrix with all rows set to zero other than ‘s’ randomly chosen ones. L~ opt = arg min M subject to P er ≥ P erTH (2) L~ where M is some cost metric of the circuit (like area or power) III. SIMULATION FRAMEWORK:HEARING AID that needs to be minimized and P erTH denotes a threshold ARCHITECTURE,HEARING LOSS MODEL,COGNITIVE for minimum acceptable perceptual quality. MODEL AND GAIN/ERROR ESTIMATOR B. Greedy Optimization Algorithm To demonstrate the operation of this algorithm, we choose a The optimization problem posed in (2) can be solved by a digital hearing aid, which interacts with our auditory sense, as modified greedy version of a gradient descent approach with the platform. Figure 1 depicts our entire simulation framework the following features: implemented in MATLAB that includes the hearing aid archi- • We evaluate only s(< NG) randomly selected compo- tecture, hearing loss model, cognitive model and gain/error nents of the gradient and this random candidate set is estimator.
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