### Table 9. Literalcountsfortheproposedtechniquecomparedwith pseudorandom testing.

"... In PAGE 15: ... In addition, such a special-purpose FSM wouldbespecifictoasingleCUT;ontheotherhand, the decoder DC for the proposed scheme is shared among multiple CUTs, thereby reducing overall TGC over- head. Table9 compares the overhead of the proposed de- terministic BIST scheme with the overhead of a pseu- dorandom BIST method [6] for several circuits. The... ..."

### Table 2: Comparison of randomness: Antirandom vs. Pseudorandom

"... In PAGE 2: ... The successive vectors in the time sequence are listed sequentially. Table2 , shows that the antirandom sequence is more random than the the pseudorandom sequences. There are several formal tests for randomness.... ..."

### Table 3: Comparison of Existing Modes of Operation with proposed Compaction/Randomization Mode. independent, (vi) there is no cell-to-cell dependency (no feedback from previous cells), and (vii) it is highly scalable (i.e., cells from the same stream can be ciphered and deciphered in paral- lel). Table 3 summarizes a comparison of the proposed randomization/compaction mode with some well-known existing modes, namely, ECB, CBC, CFB, OFB, and Counter. The presented scheme is seen to be superior to known existing modes of operation such as ECB, CBC, CFB, OFB, and Counter, each of which has only some of the above-listed attractive features. Based on the proposed technique of compression/randomization for encryption, the paper also presented a secure mechanism for in-band synchronization of encryption/decryption key updates. The mechanism used a marker cell within the data channel, whose original data payload that contains the old and new keys is pseudo-randomized and subsequently block ciphered before transmission. An important aspect of the solution is the ability to distinguish the marker cell from the other encrypted cells by encoding the information in the data eld as to whether the 16

### Table 3: Results for pseudorandom o -line testing

1998

"... In PAGE 7: ...3 groups 4 groups 5 groups 6 groups 7 groups 0:001 0:002 0:003 0:004 0:005 c3540 c880 c2670 k 0:08 0:06 0:04 0:02 0 P(Gk) Figure 7: Dependencies between k and P(Gk) Table3 shows that the average value 3:59% for q1 is reduced to 0:2% for q2. The value qk = 0 will be achieved for at most k = 3 compacted outputs.... ..."

Cited by 5

### Table 1. Pseudo-random testability bench

"... In PAGE 3: ...number of the CUT inputs. The results of a simulation of a selected set of benchmarks are shown in Table1 . The i column shows the number of the benchmark inputs, range indicates the range of the encountered number of the test patterns to fully test the circuit (in those 1000 samples), while the statistic average value is shown in the last column.... ..."

Cited by 2

### Table 1. Pseudo-random testability bench

"... In PAGE 3: ... The number of LFSR bits was set to be equal to the number of CUT inputs. The results of a simulation of a selected set of benchmarks are shown in Table1 . The i column shows the number of the benchmark inputs (including the scan path for sequential circuits), range indicates the range of the encountered number of test patterns to fully test the circuit (in those 1000 samples), while the statistical average value is shown in the last column.... ..."

### Table 6 Estimates of Hardware Overhead for Pseudorandom Testing

### Table 2: Overview of pseudo-random generator constructions.

in Graph Nonisomorphism Has Subexponential Size Proofs Unless The Polynomial-Time Hierarchy Collapses

1999

"... In PAGE 2: ... We formally define the notion of a success pred- icate in Section 4. If we can decide the success predicate of a ran- domized process with polynomial size B-oracle circuits, then the hardness assumption on the left-hand side of Table2 provides a pseudo-random generator G with the characteristics on the right- hand side of Table 2 for derandomizing the process. The symbol A in Table 2 represents an arbitrary class of oracles.... In PAGE 2: ... We formally define the notion of a success pred- icate in Section 4. If we can decide the success predicate of a ran- domized process with polynomial size B-oracle circuits, then the hardness assumption on the left-hand side of Table 2 provides a pseudo-random generator G with the characteristics on the right- hand side of Table2 for derandomizing the process. The symbol A in Table 2 represents an arbitrary class of oracles.... In PAGE 2: ... If we can decide the success predicate of a ran- domized process with polynomial size B-oracle circuits, then the hardness assumption on the left-hand side of Table 2 provides a pseudo-random generator G with the characteristics on the right- hand side of Table 2 for derandomizing the process. The symbol A in Table2 represents an arbitrary class of oracles. To illustrate the power of our generalization, we apply it to the following randomized processes from different areas of theoretical computer science.... In PAGE 5: ...heorem 4.2 Let A be a class of oracles and B an oracle. Let (F; ) be a randomized process using a polynomial number of ran- dom bits, and suppose that B can efficiently check (F; ). Then the hardness conditions on the left-hand side of Table2 provide pseudo-random generators G with complexity and seed lengths s as specified on the right-hand side of the table such that for some constant d gt; 0 and any input x of length n j Pr [ (x; ) = 1] ? Pr [ (x;Gnd( )) = 1]j 2 o(1): The parameter s in Table 2 can be any space constructible function. In order to reduce the randomness of a randomized process, we will first analyze the complexity of an oracle B capable of effi- ciently checking the associated success predicate and then con- struct a pseudo-random generator secure against B based on a func- tion with presumed hardness against B.... In PAGE 5: ...heorem 4.2 Let A be a class of oracles and B an oracle. Let (F; ) be a randomized process using a polynomial number of ran- dom bits, and suppose that B can efficiently check (F; ). Then the hardness conditions on the left-hand side of Table 2 provide pseudo-random generators G with complexity and seed lengths s as specified on the right-hand side of the table such that for some constant d gt; 0 and any input x of length n j Pr [ (x; ) = 1] ? Pr [ (x;Gnd( )) = 1]j 2 o(1): The parameter s in Table2 can be any space constructible function. In order to reduce the randomness of a randomized process, we will first analyze the complexity of an oracle B capable of effi- ciently checking the associated success predicate and then con- struct a pseudo-random generator secure against B based on a func- tion with presumed hardness against B.... In PAGE 5: ... 5 More Applications We will now apply the general framework of Section 4 to various other constructions in computational complexity. As customary, we only state our results in terms of the strongest of the assumptions in Table2 , yielding polynomial time deterministic simulations. It should be noted, however, that weaker assumptions can be taken (e.... ..."

Cited by 72

### Table 1. Comparison with Weighted Pseudo-Random Pattern Methods

"... In PAGE 7: ...Pseudo-Random Pattern Methods Table1 compares the rectangle mapping method described in this paper with weighted pseudo-random pattern methods. The fault coverage is the same for all methods: 100% of detectable single stuck-at faults.... ..."