A Low-Power Low-Memory Real-Time ASR System

Outline Overview of Automatic Speech Recognition (ASR) systems Sub-vector clustering and parameter quantization Custom arithmetic back-end Power simulation

ASR System Organization Front-end –Transform signal into a set of feature vectors Back-end –Given the feature vectors, find the most likely word sequence –Accounts for 90% of the computation Model parameters –Learned offline Dictionary –Customizable –Requires embedded HMMs Goal: Given a speech signal find the most likely corresponding sequence of words

Embedded HMM Decoding Mechanism 1T open the window

ASR on Portable Devices Problem –Energy consumption is a major problem for mobile speech recognition applications –Memory usage is a main component of energy consumption Goal –Minimize power consumption and memory requirement while maintaining high recognition rate Approach –Sub-vector clustering and parameter quantization –Customized architecture

Outline Overview of speech recognition Sub-vector clustering and parameter quantization Custom arithmetic back-end Power simulation

Sub-vector Clustering Given a set of input vectors, sub-vector clustering involves two steps: 1) Sub-vector selection: find the best disjoint partition of each vector into M sub-vectors 2) Quantization: find the best representative sub- vectors (stored in codebooks) Special cases –Vector quantization: no partition of the vectors (M=1) –Scalar quantization: size of each sub-vector is 1 Two methods of quantization –Disjoint: a separate codebook for each partition –Joint: shared codebooks for same size sub-vectors

Why Sub-vector Clustering? Vector quantization –Theoretically best –In practice requires a large amount of data Scalar quantization –Requires less data –Ignores correlation between vector elements Sub-vector quantization –Exploits dependencies and avoids data scarcity problems

Algorithms for Sub-vector Selection Doing an exhaustive search is exponential. We use several heuristics Common feature of these algorithms: the use of entropy or mutual information as a measure of correlation Key idea: choose clusters that maximize intra- cluster dependencies while minimizing inter- cluster dependencies

Algorithms Pairwise MI-based greedy clustering –Rank vector component pairs by MI and choose combination of pairs that maximizes overall MI. Linear entropy minimization –Choose clusters whose linear entropy, normalized by the size of the cluster, is the lowest. Maximum clique quantization –Based on MI graph connectivity

Experiments and Results Quantized parameters: means and variances of Gaussian distributions. Database: PHONEBOOK, a collection of words spoken over the telephone Baseline word error rate (WER): 2.42% Memory savings: ~ 85% reduction (from 400KB to 50KB) Best schemes: –Normalized joint scalar quantization, disjoint scalar quantization. –Schemes such as entropy minimization and the greedy algorithm did well in terms of error rate but at the cost of a higher memory usage.

Outline Overview of speech recognition Sub-vector clustering and parameter quantization Custom arithmetic back-end Power simulation

Custom Arithmetic IEEE Floating-point –Pros: precise data representation and arithmetic operations –Cons: expensive computation and high bandwidth Fixed-point DSP –Pros: relatively efficient computation and low bandwidth –Cons: loss of information potential overflows Still not efficient in operation and bandwidth use Custom arithmetic via table look-ups –Pros: compact representation with varied bit-widths fast computation –Cons: loss of information due to quantization overhead storage for tables complex design procedure

General Structure Idea: replace all two-operand floating-point operations with customized arithmetic via ROM look-ups –Example: Procedure: –Codebook design Each codebook corresponds to a variable in the system The bit-width depends on how precise the variable has to be represented –Table design Each table corresponds to a two-operand function The table size depends on the bit-widths of the indices and the entries

Custom Arithmetic Design for the Likelihood Evaluation Issue: bounded accumulative variables –Accumulating iteratively with a fixed number of iterations –Large dynamic range, possibly too large for single codebook Solution: binary tree with one codebook per level Y X Y t+1 = Y t + X t+1 t = 0, 1, …,D

Custom Arithmetic Design for the Viterbi Search Issue: unbounded accumulative variables –Arbitrarily long utterances; unbounded number of recursions –Unpredictable dynamic range, bad for codebook design Solution: normalized forward probability –Dynamic programming still applies –No degradation in performance –A bounded dynamic range makes quantization possible

Optimization on Bit-Width Allocation Goal: –find the bit-width allocation scheme (bw 1, bw 2, bw 3, …, bw L ) which minimizes the cost of resources while maintaining the baseline performance Approach: greedy algorithms –Optimal: intractable –Heuristics: Initialize (bw 1, bw 2, bw 3, …, bw L ) according to single-variable quantization results. Increase the bit-width of the variable which gives the best improvement concerning both performance and cost, until the performance is as good as the baseline

Three Greedy Algorithms Evaluation method: gradient Algorithms –Single-dimensional increment based on static gradient –Single-dimensional increment based on dynamic gradient –Pair-wise increment based on dynamic gradient

Results on Single-Variable Quantization

Results Likelihood evaluation: –Replace floating-point processor with only 30KB for table storage, while the baseline recognition rate was maintained –Reduce the offline storage for model parameters from 400KB to 90 KB –Reduce the memory requirement for online recognition by 80% Viterbi search: Currently we can quantize forward probability with 9 bits; Can we hit 8 bits?

Outline Overview of speech recognition Sub-vector clustering and parameter quantization Custom arithmetic back-end Power simulation

Simulation Environment SimpleScalar Cycle-level performance simulator · 5-stage pipeline · PISA instruction set (superset of MIPS-IV) · Execution-driven · Detailed statistics Wattch Parameterizable power model · Scalable for processes · Accurate to 10% versus lower-level models Binary program Hardware config Hardware access statistics Power estimate Performance estimate

Our New Simulator ISA extended to support table look-ups Three-operand instructions but need 4 values for quantization –Two inputs –Output –Table to use Two options proposed: –One-step look-up -- different instruction for each table –Two-step look-up Set active table, used by any quantizations until reset Perform look-up

Future Work Immediate future –Meet with architecture groups to discuss relevant implementation details –Determine power parameters for look-up tables Next steps –Generate power consumption data –Work with other groups for final implementation