Reducing the Test Data Volume by Enhanced Compression Code

GRD Journals | Global Research and Development Journal for Engineering | International Conference on Innovations in Engineering and Technology (ICIET) - 2016 | July 2016

e-ISSN: 2455-5703

Reducing the Test Data Volume by Enhanced Compression Code 1Devi

Sowndarya K.K. 2Kalamani.C 3Dr.K.Paramasivan 1,2,3 Department of Electronics and Communication Engineering 1,2 Dr.Mahalingam College of Engineering and Technology, Pollachi-642002 3Karpagam College of Engineering, Coimbatore-641032 Abstract Because of the increased design complexity and advanced fabrication technologies, number of tests and corresponding test data volume increases rapidly. As the large size of test data volume is becoming one of the major problems in testing System on- aChip (SOC). Test data volume reduction is an important issue for the SOC designs. Several compression coding schemes had been proposed in the past. Run Length Coding was one of the most familiar coding methodologies for test data compression. Golomb coding was used in existing compression side. The compression ratio of golomb code was found to be lesser than the combined Alternative Variable Run-length code (AVR) and nine code compression (9C) methods. The proposed combined AVR and 9C codes are used for reducing the test data volume. The experiment is conducted for proposed methods using ISCAS’89 benchmark circuits. The experimental results shows that, the proposed method is highly efficient when compared with the existing methods.

Keyword- SOC, Integrated coding, FSM, Test Data Compression, efficiency __________________________________________________________________________________________________

I. INTRODUCTION The complexity of VLSI continues to grow; more number of transistors is integrated on a single chip and test data volume has drastically increased. The testing cost and testing power are two major issues in the current generation integrated chip testing.

Testing cost is related to test data volume. The cost includes a number of parameters, but the major one is the cost of Automatic Test Equipment proposed by Pranab.K. Nag et al. Such it is difficult to transmit huge test data from ATE to system-on-a-chip (SOC). The commercial ATE’s have limited memory, bandwidths and I/O channel capacity. Testing cannot precede any faster than the amount of time required to transfer the data: Test time >= (amount of test data on tester)/ (number of tester channels)*(tester clock rate) As we can see from the above equation that the test time is directly proportional to the test data hence we can reduce this test data to reduce testing time proposed by Pranab.K. Nag et al. The testing time of SOC directly impacts the test cost. It is determined by several factors, including the test data volume, the test required to transfer test data to the cores and the maximum scan chain length. While test data volume reduction techniques can be applied to soft and hard cores, scan chains cannot modified in hard (IP) cores. New techniques are therefore needed to reduce the test data volume, decrease testing time, and overcome ATE memory limitations for SOCs containing IP cores.

Build-in self-test (BIST) proposed by S.Lei et al has emerged as an alternative to ATE-based external testing. It allows precomputed test sets to be embedded in the test sequences generated by on-chip hardware, supports test reuse and at speed testing.

Test data compression offers a promising solution to the problem of reducing the test data volume, special when the cores are not BIST ready.

The test volume reduction consists of compressing the original test data, storing the compressed data in ATE, and then decompressing them for restoring the original test volume. Three basic methods for reducing test data compression: proposed by N.A.Tauba and Abramovici.M et al Code-based schemes, Linear-decompression-based schemes and Broadcast-scan-based schemes. An alternative approach for reducing test data volume for SOCs is based on the use of data compression techniques such as: proposed by N.A.Tauba Run length based, Dictionary based, Statistical codes and Constructive codes. In this paper we will concentrate on run length based codes. The proposed work has been compared with Golomb code was proposed by Priyanka Kalode et al, AVR was proposed by B.Ye, FDR was proposed by A.Chandra et al, EFDR was proposed by H.Aiman et al and Nine code compressions was proposed by Usha S. Mehta et al. All these are variable to variable run length code. Golomb code, AVR and nine code compressions are discussed in details so that there functionality is clear as the proposed work is based on these codes.

The organization of the rest of the paper is as follows: Section II contains the discussions of Golomb and AVR codes. Section III contains the discussion of Enhanced compression code (ECC). Section IV contains the Decompression Architecture. Section V shows some of the Parameter Analysis. Finally section VI shows the Conclusion and Future Work.

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Reducing the Test Data Volume by Enhanced Compression Code (GRDJE / CONFERENCE / ICIET - 2016 / 074)

II. RUN LENGTH BASED CODES Run-length coding is a form of data compression in which runs of data are stored as a single data value and count, rather than as the original run. One particular variable-to-fixed coding scheme, the symbols consist of runs of consecutive 0’s or 1’s. Various coding schemes have been discussed in section 1. Three of which are discussed below.

A. Golomb code Golomb coding is lossless data compression algorithm. The Golomb code code was proposed by Priyanka Kalode et al that encode runs of 0s with variable-length code words. The code words are divided into groups of equal size M (M is power of 2). Golomb coding is implemented using following three steps: 1) Fix parameter M to an integer value.

2) For N, run length to be encoded find Quotient, q= int [N/M] Remainder, r= N modulo M. 3) Codeword generation: Code format=<Quotient code><remainder code> where, Quotient code: Quotient is represented in unary code. In this we get unary code by representing q strings of 0’s followed by 1.

Remainder code: Remainder is represented in truncated binary code.

If M is power of 2 then code remainder as binary format using log2M bits. If m is not a power of 2, set b= . If r<2b-M code r as plain binary using b-1 bits.

Group A1

A2

A3

….

Run Group Tail Codeword length prefix 0 0 00 000 1 01 001 2 10 010 3 11 011 4 01 00 0100 5 01 0101 6 10 0110 7 11 0111 8 001 00 00100 9 01 00101 10 10 00110 11 11 00111 …. …. …. ….

Table 1: Golomb coding for M=4.

EXAMPLE: TD: 00000011111111111100001000100000001111100001 No. of bits:44, M=4 TE: 1010 000 000 000 000 000 000 000 000 000 000 000 000 100 011 1011 000 000 000 000 1000 Encoded bits=67.

B. Alternative variable length code The alternative variable run-length code is also a variable to variable length code, consisting of two parts – the group prefix and tail was proposed by B.Ye. The prefix identifies the group in which the present run-length lies and the tails tells about the number of ones or zeros (run length) within the group. The difference between other variable run-length codes and this code was that there were two group prefixes in each group. The second prefix was the inverted data of first prefix in the same group. Each prefix was associated with half of the data from the group. The test data can be classified into runs of zero’s ending with a one and runs of one’s ending with a zero.

Group A1

A2

Run length 1 2 3 4 5 6 7 8

Group prefix

Tail

01

0 1 0 1 00 01 10 11

10 001

Code word 010 011 100 101 00100 00101 00110 00111

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Reducing the Test Data Volume by Enhanced Compression Code (GRDJE / CONFERENCE / ICIET - 2016 / 074)

9 110 00 10 01 11 10 12 11 13 0001 000 14 001 ...... ......

20 111 21 1110 000 22 001 …. …..

Table 2: AVR code compression.

A3

11000 11001 11010 11011 0001000 0001001 ......

0001111 1110000 1110001 …..

EXAMPLE: TD : 00000011111111111100001000100000001111100001 No. of bits: 44 TE : 001011101010010000110101101 No. of bits : 27

III. ENHANCED COMPRESSION CODE The drawback of the Golomb code is that it is beneficial only for the runs of zeros. This was evident from the experimental results.

Hence it was needed to encode runs of ones also. Although AVR was efficient enough in encoding ones but move from one group to another it increments two bits in the code word. The codes are used depending on the run length. Now AVR and Nine code compression is merged. The code provides minimum number of bits to a particular run length is used.

A. Nine code compression Nine-coded compression technique was proposed by Usha S. Mehta et al considers the input test data block size of fixed length.

Each input test vector is partitioned into groups of bits with particular size called as block; say K and the block size is user defined.

The block size is selected as even, so that each of these blocks can easily be divided into equal halves such that the halves may be all 0s, 1s or a set of mismatched bits, that is, a mixed group of 0s, 1s and X-bits.

Cases 1 2

Input block 0000 0000 1111 1111

Symbol 00 11

Code word 0 10

3 4

0000 1111 1111 0000

01 10

11000 11001

5 6 7 8

1111 uuuu uuuu 1111 0000 uuuu uuuu 0000

1u u1 0u u0

11010 11011 11100 11101

9

uuuu uuuu

uu

1111

Table 3: Nine code compression.

EXAMPLE: TD: 001011101010010000110101101. No. of bits = 27 TE: 1111111111111111. No. of bits = 16

Therefore the compression has improved by 63.3%.

IV. DECOMPRESSION ARCHITECTURE A. Decoder of AVR The on-chip decoder decompresses the encoded test set TE and produces the primary test set TD. The decoder architecture is similar to the on-chip decode architecture of Golomb code and AVR code. The testing time was reduced because test pattern decompression could be carried out at higher clock frequencies. The lmax is the longest run of zeros or 1s in TD, and k=┌log2(lmax+4)-2┐. The decoder is made up of a k+1- bit counter, a log2 (k+1) bit counter, a T flip flop and an exclusive or gate. It has following signal.  The select line tells us which code was used for encoding. Bypass signal reflects the bypass mode.  Bit_in is the input of FSM and an enable signal en was used to control the input encoded data when the decoder was ready.  Signal shift was used to control the prefix and tail of the codeword to shift into the (k+1)-bit counter.  Signal dec1 was used for decrement, and rs1 was used to indicate the reset state of the counter.

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Reducing the Test Data Volume by Enhanced Compression Code (GRDJE / CONFERENCE / ICIET - 2016 / 074)



The second counter of log2 (k+1) bit was used to count the length of the prefix and tail in order to identify the group. The signals inc and dec2 were used to increment and decrement the counter, respectively.  The FSM’s output signal out controlled the toggle of the T flip-flop, and indicated that it had finished decoding runs of 0s or 1s before decoding runs of 1s or 0s according to the binary parameter α. Signal v indicated when the output was valid and it was used to control the scan clock signal scan_clk.

The operation of decoder was expressed as follows:  Step 1: In the initial state, the T flip-flop was reset to 0. Signal en become high and ready to receive data from bit_in.  Step 2: The FSM fed the (k+1)-bit counter with the prefix. The end of the prefix was identified by the separator 0 or 1 according to code word type. The signal en, shift and inc were kept high until 0 or 1 was received. If the prefix was inverted in the (k+1) bit counter. For example, if the prefix was “1110”, then the data in the (k+1)-bit counter would be “0001”.  Step 3: The FSM output, 0s and 1s decrement the (k+1)-bit counter and made the signal dec1 high. It continued it until rs1 is high. The v signal shows whether the output was high or not. The tail part was again shifted to (k+1)-bit counter, but it was under the log2 (k+1)-bit counter. It controlled the length of the control word.

Fig. 1: Decoder Architecture.

B. Decoder of Combined AVR and 9c It consists of two Finite-State Machine (FSM) blocks, one synchronization block, one counter, a MUX, and the control signals.

The decoder operates on two clocks – the external clock ATE_CLK and the internal clock SOC_CLK. The FSM1 is of 9C and FSM2 is of AVR. The FSM1 receives the compressed data, DATA_IN from the ATE at ATE_CLK frequency. Once the FSM1 detects the code word, decoding begins at the system clock frequency (SOC_CLK) and the DEC_EN is set to 1. When FSM1 decodes the data, it does not receive any data from the ATE. The ACK_H is set to 1, as soon as the FSM1 decoded the code word and it is ready to receive the next code word. The FSM2 receives the decoded data from FSM1 at the frequency of the system clock was proposed by Shurti Chadha et al and Usha S.Mehta et al.

Fig. 2: Decoder of combined AVR and 9C.

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Reducing the Test Data Volume by Enhanced Compression Code (GRDJE / CONFERENCE / ICIET - 2016 / 074)

V. PARAMETER ANALYSIS Power dissipation is an important problem for the circuit under test. A huge amount of power is dissipated when the circuit elements switched from logic 1 to 0 and vice versa. Next, we analyzed the testing time when a single scan chain was fed by the AVR decoder.

Test data compression decreased testing time and allowed the use of a low-cost ATE, running at a low frequency, to the core without imposing any penalties on the total testing time. The efficiency is used to compare the different codes with each other, where Efficiency=

(Total no.bits inTD−Total no.bits in TE ) (Total no.bits inTD) TD −TE

=

TD

× 100

× 100

Where TD is the original test data and TE is the data archived after compression.

Fig. 3: Schematic Diagram of combined AVR and 9C. Parameters

Nine code

Combined AVR and 9C

Area (µm)sq

Alternative variable runlength code 6866

350

7437

Time (ps)

142

171

381

Gate Count (µm) sq Power (nw)

6866.334

350.208

7437.132

50165 4688 53191 Table 4: Decompression Results.

Fig. 4: Simulation results for compared codes

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Reducing the Test Data Volume by Enhanced Compression Code (GRDJE / CONFERENCE / ICIET - 2016 / 074)

Circuit

Original number of bits

FDR % compressed

EFDR compressed

%

Golomb code compressed

%

Comb AVR and 9C % compressed

s5378 s9234

23754 39273

48.02 43.59

51.92 45.89

-3.23 36.80

65.7 74.08

s15850 s38417

76986 164736

66.22 43.26

67.99 60.56

63.49 86.01

86.06 96.95

Table 5: Compression results for compared codes.

VI. CONCLUSION AND FUTURE WORK Proposed data compression techniques are implemented using MATLAB 13.0 language, Xilinx 14.2, schematic diagram is taken using Cadence, the experiment is conducted using large ISCAS’89 benchmark circuits and the results show that there is a significant improvement in the compression ratio. So this technique combined with other techniques gives better compression. The ISCAS’89 sequential benchmark circuits are used here. So only specified bits are considered. The future work is carried out for test data compression scheme based on Compatible data block coding.

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