Interview Question · Concept

Dynamic Array, Queue or Associative Array

L0 FresherL1 Juniorsystemverilogarraysdata structures

Asked at Google

The interviewer asks

When do you use a dynamic array, a queue and an associative array in a testbench? Give one real use of each.

Reveal the Model Answer

A dynamic array gets its size at run time and keeps it until you resize it. It fits a packet payload whose length is known when the packet is built. A queue grows and shrinks at both ends, so it fits an in-order scoreboard of expected items. An associative array stores only the keys you write. It fits a sparse memory model, or open requests looked up by transaction ID.

Outstanding Reads Looked up by IDAn associative array keyed by read ID holds only the IDs still open. Arrows from the checker to the map are calls; the tokens coming back are what they return. Other arrows show data.

Store three reads
Three requests create three keys, and num() returns 3. The key type allows 4 billion IDs, but only these three entries exist.
Follow the calls, values and waits
1. data ID 3: a3: Requests → exp_data [int]
2. data ID 5: b5: Requests → exp_data [int]
3. data ID 1000: c0: Requests → exp_data [int]
ID 5 answers
exists(5) returns 1, so the checker reads b5, compares it with the response and prints a match. Then it deletes key 5.
Follow the calls, values and waits
1. data ID 5: b5: Responses → on_response()
2. call exists(5): on_response() → exp_data [int]
3. return 1: exp_data [int] → on_response()
4. data b5: exp_data [int] → on_response()
5. data ID 5: match: on_response() → Log
ID 9 was never issued
exists(9) returns 0. The checker reports an unexpected response before reading anything, so the default value 0 never reaches a comparison.
Follow the calls, values and waits
1. data ID 9: 0: Responses → on_response()
2. call exists(9): on_response() → exp_data [int]
3. return 0: exp_data [int] → on_response()
4. data ID 9: unexpected response: on_response() → Log
ID 3 has wrong data
Key 3 exists, but its expected data, a3, differs from the response, a4. The checker reports both values and deletes the key.
Follow the calls, values and waits
1. data ID 3: a4: Responses → on_response()
2. call exists(3): on_response() → exp_data [int]
3. data a3: exp_data [int] → on_response()
4. data ID 3: expected a3 got a4: on_response() → Log
ID 5 again
Key 5 was deleted after its first answer, so the second response for ID 5 is caught as unexpected.
Follow the calls, values and waits
1. data ID 5: b5: Responses → on_response()
2. call exists(5): on_response() → exp_data [int]
3. return 0: exp_data [int] → on_response()
4. data ID 5: unexpected response: on_response() → Log
End of test
One key is left. foreach lists ID 1000, a read whose response never came.
Follow the calls, values and waits
1. data ID 1000: exp_data [int] → End of test

Read the diagram as text

Requests: IDs 3, 5 and 1000
exp_data [int]: one key per outstanding read
on_response(): exists, compare, delete
Responses: IDs 5, 9, 3, then 5 again
End of test: foreach lists leftover keys
Log: one line per response

Requests → exp_data [int]: store
on_response() → exp_data [int]: exists, read, delete
Responses → on_response(): response
on_response() → Log
exp_data [int] → End of test: leftover keys

Check presence with exists before every read, and delete each key once it has been answered.

	Dynamic `d[]`	Queue `q[$]`	Associative `a[key]`
Size	Set by `new[n]`	Grows with push, shrinks with pop	Number of keys written
Index	0 to n-1	0 to `$` (last)	Any integral, string or class key
Cheap operations	Index	Push and pop at either end, index	Lookup, insert, delete by key
Typical TB use	Payload bytes	Expected items in order	Sparse memory, ID table

Self-Check

Tick each key point you covered in your answer.

Key point 1 A dynamic array gets its size at run time and keeps it until you resize it. Key point 2 It fits a packet payload whose length is known when the packet is built. Key point 3 A queue grows and shrinks at both ends, so it fits an in-order scoreboard of expected items. Key point 4 An associative array stores only the keys you write. Key point 5 It fits a sparse memory model, or open requests looked up by transaction ID.

What Interviewers Listen For (Fresher)

A good fresher answer gives one real testbench use for each kind. It also says which kind grows and shrinks at both ends.

Cross-Questions

Where the interviewer goes next. Open a node to practise that follow-up.

WhyWhat IfProve ItBreak ItCompareScale It

Dynamic Array, Queue or Associative Array
WhyWhy a Missing Key Reads as a DefaultL1 Junior
Why does reading a missing associative-array key return a default instead of stopping the simulation?

Reveal the Model Answer

The language treats it like an out-of-range read of any array. It returns the element type's default and may print a warning, so every expression still has a value. Stopping would make every speculative lookup fatal. The cost is that a check must call exists() when "absent" and "present with value 0" mean different things.

Self-Check
Tick each key point you covered in your answer.
Key point 1 The language treats it like an out-of-range read of any array. Key point 2 It returns the element type's default and may print a warning, so every expression still has a value. Key point 3 Stopping would make every speculative lookup fatal. Key point 4 The cost is that a check must call exists() when "absent" and "present with value 0" mean different things.

Misconception

"Reading the key creates it with value 0, so num() grows."

A read does not allocate; only a write does. The twin on the root prints one entry after reading a missing key.

Source IEEE 1800-2017 §7.8.6
- Lesson 2.3Associative Arrays and Transaction IDs
CompareGrowing a Queue vs Resizing a Dynamic ArrayL1 JuniorL2 Mid-Level
Compare appending one element at a time to a queue with growing a dynamic array by d = new[d.size() + 1](d).

Reveal the Model Answer

The resize form allocates a new array and copies every element on each call. Building n elements this way costs about n squared copies. push_back on a queue is designed for growth at the end. Use the dynamic array when the size is known once, and the queue when elements arrive one by one.

Self-Check
Tick each key point you covered in your answer.
Key point 1 The resize form allocates a new array and copies every element on each call. Key point 2 Building n elements this way costs about n squared copies. Key point 3 push_back on a queue is designed for growth at the end. Key point 4 Use the dynamic array when the size is known once, and the queue when elements arrive one by one.

Misconception

"new[n](old) resizes the array in place."

It creates a new array of size n and copies the old contents into it; the old storage is released.

Source IEEE 1800-2017 §7.5.1
Prove ItSolve 2.1.1: Resize and PreserveSimulator-graded

Show it, don't just say it: this Silicon Lab problem is graded by the simulator.
Open Problem 2.1.1
What IfWalking an ID Table with ForeachL1 JuniorL2 Mid-Level
Asked if they iterate the ID table

What if the end-of-test report walks the ID table with foreach and prints the outstanding IDs? In what order do they come out, and can you delete while walking?

Reveal the Model Answer

Keys come out in ascending key order, not insertion order; with signed int keys the standard puts negative keys first. Deleting entries inside the same foreach is undefined, so collect the keys first and delete afterwards.

Self-Check
Tick each key point you covered in your answer.
Key point 1 Keys come out in ascending key order, not insertion order; with signed int keys the standard puts negative keys first. Key point 2 Deleting entries inside the same foreach is undefined, so collect the keys first and delete afterwards.

Tool as Truth

"Verilator listed 7, 10, -2, so negative keys come last."

That is a Verilator 5.052 deviation the course measured. The standard orders signed keys numerically, so -2 comes first; unsigned key types avoid the difference.

Source Lesson 2.3 simulator note (Verilator 5.052 key order); IEEE 1800-2017 §7.9.4
- Lesson 2.3Associative Arrays and Transaction IDs
- Lesson 2.7Debug Case: Stale IDs After Reset
Prove ItSolve 2.3.1: Outstanding Request LookupSimulator-graded

Show it, don't just say it: this Silicon Lab problem is graded by the simulator.
Open Problem 2.3.1
Scale ItChoosing the Data Structure for an Out-of-Order ScoreboardL2 Mid-LevelL3 Senior
Your bus allows out-of-order responses between IDs but keeps order within one ID. How do you structure the expected-data store, and what does it cost?
2:30

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Reveal the Model Answer

Use an associative array keyed by ID, where each element is a queue. It matches the ordering rule exactly: look up the ID, compare with the front of its queue, and pop. Lookups stay cheap however many IDs are open, and a non-empty queue at the end is an outstanding request to report. Store handles in the queues rather than copies, and flush the whole table on reset.

One FIFO per IDAn associative array of queues, one FIFO per ID. Arrows show data: expected values pushed by requests, values popped by the checker, and responses from the DUT.

Requests push
The request for ID 1 pushes a1 onto exp_q[1], and the request for ID 2 pushes b2 onto exp_q[2]. Each ID has its own FIFO.
Follow the calls, values and waits
data a1: Requests → exp_q[1]
data b2: Requests → exp_q[2]
ID 2 answers first
The response for ID 2 arrives first. The checker sees that exp_q[2] is not empty, pops its front, gets b2 and reports a match.
Follow the calls, values and waits
data ID 2: b2: Responses → Checker
data b2: exp_q[2] → Checker
ID 1 answers
The response for ID 1 pops exp_q[1] and matches a1. Responses for different IDs overtook each other, and no false error appeared.
Follow the calls, values and waits
data ID 1: a1: Responses → Checker
data a1: exp_q[1] → Checker
One response too many
A second ID 1 response arrives, but exp_q[1].size() is 0. The checker reports an unexpected response instead of popping a made-up 0.
Follow the calls, values and waits
data ID 1: a1: Responses → Checker

Read the diagram as text
Requests: ID 1 expects a1, then ID 2 expects b2
exp_q[1]: FIFO for ID 1
exp_q[2]: FIFO for ID 2
Checker: pops only the queue of the response's ID
Responses: ID 2, then ID 1, then ID 1 again
Requests → exp_q[1]: push_back
Requests → exp_q[2]: push_back
exp_q[1] → Checker: pop_front
exp_q[2] → Checker: pop_front
Responses → Checker: response

Within one ID the queue still enforces request order; across IDs, responses may arrive in any order.

Self-Check
Tick each key point you covered in your answer.
Key point 1 Use an associative array keyed by ID, where each element is a queue. Key point 2 It matches the ordering rule exactly: look up the ID, compare with the front of its queue, and pop. Key point 3 Lookups stay cheap however many IDs are open, and a non-empty queue at the end is an outstanding request to report. Key point 4 Store handles in the queues rather than copies, and flush the whole table on reset.

What Interviewers Listen For (Senior)
A good senior answer builds the structure from the protocol's ordering rule. Without being asked, it adds a flush on reset and an end-of-test check that every queue is empty.
Wrong Altitude

"One global queue and search it for the matching response."

It works, but every response needs a linear search. It also cannot tell a wrong order within one ID from a legal reorder across IDs.
Break ItThe Scoreboard That Remembered Before ResetL1 JuniorL2 Mid-Level
A test applies reset in the middle of traffic. After reset, the first response for ID 3 fails although the waveform shows the DUT returned the right data for the new request. Here is the log. What is wrong?
log
```
@1200 req  id=3 data=a0
@1250 RESET asserted (2 requests outstanding)
@1400 RESET released
@1450 req  id=3 data=b1
@1520 resp id=3 data=b1
@1520 SCOREBOARD ERROR: ID 3: expected a0 got b1
```
2:30

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Reveal the Model Answer

The scoreboard still holds the expected data of the request that was in flight at reset. So it compares the new response with a0. The DUT legitimately dropped that request; the testbench did not. On reset, flush every expected-data structure, and decide whether a dropped request should be logged or counted.

An Expectation That Survives Reset

Cycle 1
Read ID 3 is issued, and the scoreboard pushes its expected data, 0x10, onto exp_q[3].
Cycle 2
Reset is asserted. The DUT drops the outstanding read, as the spec requires, but the scoreboard keeps 0x10.
Cycle 5
A new read reuses ID 3. Its expected data, 0x20, queues behind the stale 0x10.
Cycle 7
The DUT returns 0x20. The scoreboard pops the stale 0x10 and reports ID 3: expected 10 got 20.

The same bug on a waveform, with data 10 and 20. The DUT forgot the first read at reset; the scoreboard did not.

Self-Check
Tick each key point you covered in your answer.
Key point 1 The scoreboard still holds the expected data of the request that was in flight at reset. Key point 2 So it compares the new response with a0. Key point 3 The DUT legitimately dropped that request; the testbench did not. Key point 4 On reset, flush every expected-data structure, and decide whether a dropped request should be logged or counted.

What Interviewers Listen For (Junior)
A good junior answer reads the log and sees that the expected value belongs to the request from before reset. It adds a flush of the scoreboard on reset.
Misconception

"Reset only matters to the DUT; the testbench just waits for it."

Every stateful testbench component holds expectations about the DUT. The twin keeps one stale entry and fails a correct response.

Run It

An ID-keyed scoreboard across a reset that drops one outstanding request.

RUNVerilator · simulator service

Edit the code, then run it

Expected: ID 3: expected a0 got b1 mismatches=1 FAIL: the scoreboard kept the pre-reset expectation and compared the new response against it

See the Fix
module tb; // Scoreboard: expected data queued per transaction ID. logic [7:0] exp [int][$]; int mismatches = 0; task automatic expect_req(int id, logic [7:0] d); exp[id].push_back(d); endtask task automatic got_resp(int id, logic [7:0] d); logic [7:0] e = exp[id].pop_front(); if (e !== d) begin mismatches++; $display("ID %0d: expected %h got %h", id, e, d); end endtask initial begin expect_req(3, 8'hA0); // request in flight ... // ... reset: the DUT drops every outstanding request exp.delete(); // the fix: the scoreboard forgets them too expect_req(3, 8'hB1); // ID 3 reused after reset got_resp(3, 8'hB1); // the DUT answers the new request correctly $display("mismatches=%0d", mismatches); if (mismatches) $display("FAIL: the scoreboard kept the pre-reset expectation and compared the new response against it"); else $display("PASS: the response matched the post-reset request"); end endmodule
Prints: mismatches=0 PASS: the response matched the post-reset request
Delivery

"The DUT must be wrong; the scoreboard says so."

The log shows the response matching the post-reset request. Debug starts by asking which side holds old state, not by trusting the checker.

Wrong Altitude

"Avoid reusing IDs after reset in the test."

That hides the bug instead of fixing it, and real software reuses IDs freely after a reset.
WhyWhy a Reset Drops Outstanding RequestsL1 Junior
Why is it legal for the DUT to forget requests at reset instead of finishing them?

Reveal the Model Answer

Reset returns the design's state to its defined initial values, and the queues holding outstanding requests are part of that state. Most protocols say transactions in progress at reset are abandoned and that nothing may be signalled until reset is released. The testbench must model the same loss.

Self-Check
Tick each key point you covered in your answer.
Key point 1 Reset returns the design's state to its defined initial values, and the queues holding outstanding requests are part of that state. Key point 2 Most protocols say transactions in progress at reset are abandoned and that nothing may be signalled until reset is released. Key point 3 The testbench must model the same loss.

Half-Truth

"A good design should complete in-flight requests before resetting."

That is a graceful-shutdown feature some designs add, not what reset means. Check the spec rather than assuming it.
- Lesson 2.7Debug Case: Stale IDs After Reset
- Lesson P1.3Handshakes: Request and Acknowledge, Valid and Ready
What IfA Response That Outlives ResetL1 JuniorL2 Mid-Level
What if responses can outlive reset, and the next request reuses the same ID?

Reveal the Model Answer

Then a late response for the old request can match the new request with the same ID. The scoreboard may pass wrong data, or flag correct data. Give each expected entry extra identity, or wait a quiet period after reset before reusing IDs. Test it with a reused ID and a duplicate response.

Self-Check
Tick each key point you covered in your answer.
Key point 1 Then a late response for the old request can match the new request with the same ID. Key point 2 The scoreboard may pass wrong data, or flag correct data. Key point 3 Give each expected entry extra identity, or wait a quiet period after reset before reusing IDs. Key point 4 Test it with a reused ID and a duplicate response.

Missed Corner

"Clearing the queue at reset fixes stale IDs for good."

It removes old expectations, but not responses still in flight. A late one can match a new request with the same ID.

Source Lesson 2.7 key points
- Lesson 2.7Debug Case: Stale IDs After Reset
- Problem 2.7.1Flush Pending Requests
CompareFlushing the Scoreboard vs Tagging EpochsL2 Mid-Level
Compare flushing the scoreboard on reset with tagging each expectation with a reset epoch number.
2:30

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Reveal the Model Answer

Flushing is simple and correct when the monitors also stop at reset. Tagging every entry with the epoch it was created in lets the checker recognise a late response from before the reset. It can then report that case as its own error instead of mismatching it. Use the epoch scheme when responses can still emerge from pipelines around the reset edge.

A Stale Entry, Then the Clear on ResetThe scoreboard state for ID 3, first without and then with a clear on reset. Arrows show data, except exp_q.delete(), which is a call.

Before reset
A read with ID 3 is issued before reset, and its expected data, 0x10, is pushed onto exp_q[3].
Follow the calls, values and waits
data 10: Request monitor → exp_q[3]
Reset, no clear
Reset is asserted and the DUT drops that read. Without a clear, 0x10 stays in the queue and describes a read that no longer exists.
ID 3 is reused
After reset a new read reuses ID 3. Its expected data, 0x20, lands behind the stale 0x10.
Follow the calls, values and waits
data 20: Request monitor → exp_q[3]
A false failure
The DUT returns 0x20, but pop_front hands the checker the stale 0x10. It reports expected 10 got 20, and 0x20 is left behind.
Follow the calls, values and waits
data 20: DUT → response()
data 10: exp_q[3] → response()
With the repair
Run it again with the repair. When rst_n falls, exp_q.delete() removes every ID and every entry, so 0x10 is gone.
Follow the calls, values and waits
data 10: Request monitor → exp_q[3]
call exp_q.delete(): Reset monitor → exp_q[3]
Only new reads remain
The new read pushes 0x20, and the response pops 0x20 and matches. exp_q.num() is 0 at the end.
Follow the calls, values and waits
data 20: Request monitor → exp_q[3]
data 20: DUT → response()
data 20: exp_q[3] → response()

Read the diagram as text
Request monitor: pushes expected data per ID
Reset monitor: waits for negedge rst_n
exp_q[3]: expected data for ID 3
response(): pops the front for the ID
DUT: returns ID 3 with data 20
Request monitor → exp_q[3]: push_back
Reset monitor → exp_q[3]: exp_q.delete()
exp_q[3] → response(): pop_front
DUT → response(): response

Each entry stands for a read the DUT still owes; reset cancels them all, so the scoreboard must drop them too.

Self-Check
Tick each key point you covered in your answer.
Key point 1 Flushing is simple and correct when the monitors also stop at reset. Key point 2 Tagging every entry with the epoch it was created in lets the checker recognise a late response from before the reset. Key point 3 It can then report that case as its own error instead of mismatching it. Key point 4 Use the epoch scheme when responses can still emerge from pipelines around the reset edge.
Missed Corner

"Flushing is always enough."

A response already on the wires at the reset edge arrives after the flush. It has no matching entry, or it matches a reused ID by accident.

Source Lesson 2.7 (debug case: stale IDs after reset)
- Lesson 2.7Debug Case: Stale IDs After Reset
Prove ItSolve 2.7.1: Flush Pending RequestsSimulator-graded

Show it, don't just say it: this Silicon Lab problem is graded by the simulator.
Open Problem 2.7.1
Scale ItReset in the Middle of a Test Across a Whole EnvironmentL3 Senior
How do you make mid-test reset work across a whole environment with several agents, scoreboards and running sequences?

Reveal the Model Answer

Give reset one owner that broadcasts it, and make every stateful component handle it. Drivers drop their current item and idle the bus, monitors abandon partial transactions, scoreboards flush or bump an epoch, and sequences restart. Then cover reset at each interesting point, such as mid-burst or with a full FIFO. Each component's reset code runs only in those cases.

Self-Check
Tick each key point you covered in your answer.
Key point 1 Give reset one owner that broadcasts it, and make every stateful component handle it. Key point 2 Drivers drop their current item and idle the bus, monitors abandon partial transactions, scoreboards flush or bump an epoch, and sequences restart. Key point 3 Then cover reset at each interesting point, such as mid-burst or with a full FIFO. Key point 4 Each component's reset code runs only in those cases.

What Interviewers Listen For (Senior)
A good senior answer puts mid-test reset in the test plan with its own coverage. It names the failure that happens when one component ignores reset.

Wrong Altitude

"End the test at reset and start a new one."

It avoids the problem in the bench but never tests the DUT's own reset-in-traffic behaviour, which is where real bugs hide.
Prove ItSolve 2.3.3: Out-of-Order Response MatcherSimulator-graded

Show it, don't just say it: this Silicon Lab problem is graded by the simulator.
Open Problem 2.3.3

What People Get Wrong

Common wrong answers and why they fail. Run It shows the wrong belief failing on a real simulator.

Misconception

"Reading a key that was never written is an error, so a stray ID is caught by the lookup."

The read quietly returns the element type's default. In the twin a zero-length response for an ID never requested matches that default and passes.

Run It

An ID table checked by reading exp_len[id] directly, then a response for an ID that was never requested.

RUNVerilator · simulator service

Edit the code, then run it

Expected: errors=0 entries=1 FAIL: reading a missing key returned 0, so the stray zero-length response matched

module tb;
  int exp_len [int];            // expected burst length per transaction ID
  int errors = 0;
  task check(int id, int len);
    // The belief: reading an ID that was never stored is an error, so a stray ID gets reported.
    if (exp_len[id] != len) errors++;
  endtask
  initial begin
    exp_len[7] = 4;
    check(7, 4);                // a real response
    check(9, 0);                // ID 9 was never requested; a zero-length response
    $display("errors=%0d entries=%0d", errors, exp_len.num());
    if (errors == 0) $display("FAIL: reading a missing key returned 0, so the stray zero-length response matched");
    else $display("PASS: the stray ID was reported");
  end
endmodule

See the Fix

module tb;
  int exp_len [int];            // expected burst length per transaction ID
  int errors = 0;
  task check(int id, int len);
    // The fix: ask whether the ID is outstanding before reading it.
    if (!exp_len.exists(id)) begin errors++; $display("unexpected ID %0d", id); end
    else if (exp_len[id] != len) errors++;
  endtask
  initial begin
    exp_len[7] = 4;
    check(7, 4);                // a real response
    check(9, 0);                // ID 9 was never requested; a zero-length response
    $display("errors=%0d entries=%0d", errors, exp_len.num());
    if (errors == 0) $display("FAIL: reading a missing key returned 0, so the stray zero-length response matched");
    else $display("PASS: the stray ID was reported");
  end
endmodule

Prints: unexpected ID 9 errors=1 entries=1 PASS: the stray ID was reported

Half-Truth

"Use a queue for everything; it can do what the others do."

A queue can be indexed, but finding an ID in it means a linear search per response. With thousands outstanding, the lookup cost dominates the run.

Missed Corner

"A fixed-size array of 2^32 entries models the memory."

That allocates every location up front. An associative array keyed by address stores only the addresses the test touches.

Source IEEE 1800-2017 §7.8

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